Thermotherapy reduces COVID-19 risk: real-time meta-analysis of 4 studies

@CovidAnalysis, October 2026, Version 2V2 (V1 2024)

Outcome (studies)

Improvement

Relative risk · 95% CI

All studies (4)56%

Mortality (1)43%

Ventilation (2)53%

ICU admission (1)76%

Hospitalization (1)40%

Recovery (2)67%

Viral clearance (1)80%

RCTs (3)56%

Late (4)56%

after exclusions

00.511.5+

← Thermotherapy
reduces riskIncreased
risk →

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Abstract

Significantly lower risk is seen for recovery. 3 studies from 3 independent teams in 2 countries show significant benefit.

Meta-analysis using the most serious outcome reported shows 56% [9‑78%] lower risk. Results are similar for Randomized Controlled Trials and higher quality studies.

Outcome (studies)

Improvement

Relative risk · 95% CI

All studies (4)56%

Mortality (1)43%

Ventilation (2)53%

ICU admission (1)76%

Hospitalization (1)40%

Recovery (2)67%

Viral clearance (1)80%

RCTs (3)56%

Late (4)56%

after exclusions

00.511.5+

← Thermotherapy
reduces riskIncreased
risk →

Currently there is limited data, with only 217 patients and only 20 control events for the most serious outcome in trials to date.

No treatment is 100% effective. Protocols combine safe and effective options with individual risk/benefit analysis and monitoring. Thermotherapy currently has no early treatment studies. Thermotherapy methods may have additional mechanisms of action beyond increased internal body temperatures. Studies of ventilated patients are excluded1. All data and sources to reproduce this analysis are in the appendix.

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Thermotherapy for COVID-19 — Highlights

Thermotherapy reduces risk with high confidence for pooled analysis, low confidence for ICU admission and recovery, and very low confidence for mortality.

54th treatment shown effective in December 2023, now with p = 0.026 from 4 studies.

Real-time updates and corrections with a consistent protocol for 227 treatments. Outcome specific analysis and combined evidence from all studies including treatment delay, a primary confounding factor.

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October 2026

Thermotherapy COVID-19 studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Huang (RCT)80%0.20 · 0.01–3.97oxygen0/252/25diathermy Dominguez-Nicolas53%0.47 · 0.27–0.81no improv.8/175/5LF-ThMS Tian (DB RCT)84%0.16 · 0.02–1.40ventilation1/273/13diathermy Bonfanti (RCT)−11%1.11 · 0.39–3.19death4/94/10EXCLUDEDVentilated patients Mancilla-G.. (RCT)43%0.57 · 0.22–1.45death6/5410/51TherMoCoVheating pad

Late treatment56%0.44 · 0.22–0.9115/12320/9456% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.026

All studies56%0.44 · 0.22–0.9115/12320/9456% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.02600.511.52+

Rotate screen for more detailsIncrease width for more details

← Thermotherapy
reduces riskThermotherapy
increases risk →

Fig. 1. A. Random-effects meta-analysis. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix. B. Timeline of results in thermotherapy studies. The marked dates indicate the time when efficacy was known with a statistically significant improvement of ≥10% from ≥3 studies for pooled outcomes and pooled outcomes in RCTs.

Introduction

Fig. 2. SARS-CoV-2 spike protein fibrin binding leads to thromboinflammation and neuropathology, from2.

Immediate treatment recommended

SARS-CoV-2 infection primarily begins in the upper respiratory tract and may progress to the lower respiratory tract, other tissues, and the nervous and cardiovascular systems, which may lead to cytokine storm, pneumonia, ARDS, neurological injury3-19 and cognitive deficits6,11, cardiovascular complications20-26, DNA damage27-30, organ failure, and death. Even mild untreated infections may result in persistent cognitive deficits31—the spike protein binds to fibrin leading to fibrinolysis-resistant blood clots, thromboinflammation, and neuropathology. Minimizing replication as early as possible is recommended.

Many treatments are expected to modulate infection

SARS-CoV-2 infection and replication involves the complex interplay of 500+ host and viral proteins and other factorsA,32-39, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 12,000 compounds may reduce COVID-19 risk40, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.

Thermotherapy

Thermotherapy, or heat therapy includes hydrothermotherapy, hydrotherapy, and diathermy, methods for increasing internal body temperature which may have benefits similar to natural fever, while providing potential advantages regarding localization, precision, and lower metabolic cost. Thermotherapy is known to modulate the immune system41 and to minimize SARS-CoV-2 replication42.

Other infections

Studies have shown efficacy with thermotherapy for pneumonia43, the common cold44, SARS-CoV-145, and influenza46.

Analysis

We analyze all significant controlled studies of thermotherapy for COVID-19, excluding studies with mechanically ventilated patients. Search methods, inclusion criteria, effect extraction criteria (more serious outcomes have priority), all individual study data, PRISMA answers, and statistical methods are detailed in Appendix 1. We present random-effects meta-analysis results for all studies, individual outcomes, Randomized Controlled Trials (RCTs), and higher quality studies.

Treatment timing

Fig. 3 shows stages of possible treatment for COVID-19. Prophylaxis refers to regularly taking medication before becoming sick, in order to prevent or minimize infection. Early treatment refers to treatment immediately or soon after symptoms appear, while late treatment refers to more delayed treatment. For thermotherapy, we do not consider prophylaxis. Currently all thermotherapy studies use late treatment.

Fig. 3. Treatment stages.

Evaluation Notes

Analyzing the data from first principles is always important, and may be especially important for COVID-19. Bias in the design, analysis, and reporting of clinical studies has long been reported, and may be increased for COVID-19 due to politicization. For example, Scott Alexander noted that "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."47 Social pressure to report politically acceptable results was very strong. There may therefore be unusually strong bias in design, analysis, and reporting. For example, Prof. Greenland reports that Hayward et al. applied an unjustified and undisclosed extremely tight, null-centered Bayesian prior that biases towards more politically acceptable results49. While the politicization focused on certain treatments, it was not limited to them. For example, media coverage was highly biased against positive results for low-cost treatments50 across the 227 treatments we cover. Studies should be evaluated from first principles, incorporating the treatment delay, treatment regimen, patient population, and other confounding factors. For COVID-19, there is no significant difference in the results of RCTs compared to observational studies, RR 0.97 [0.91‑1.03]51—in both cases bias varies from minimal to extreme, and all studies must be evaluated individually.

US authorities claim only three high-profit drugs from companies with strong US lobbying are beneficial for early treatment (2 repurposed drugs - remdesivir and molnupiravir, and one novel drug - nirmatrelvir)B. COVID-19 involves the interplay of many viral and host proteins and factors, providing over 500 therapeutic targets52. Given the 12,000+ proposed treatments40, existing results for many treatments with overlapping secondary complications and therapeutic targets, and the combination of known safety and pharmocokinetics for certain treatments along with strong mechanistic evidence, the probability of only 3 high-profit drugs from top lobbying companies being beneficial is extremely low. This likely reflects a bias toward evaluation of drugs with strong financial resources and efforts devoted to passing the US approval process, and very limited or non-existing evaluation of low-cost treatments. Indeed, official recommendations varied widely between countries53. Some countries evaluated limited low-cost treatments early and 25 low-cost treatments were approved in one or more countries53.

Contrary to claims found online, we analyze both all studies and higher-quality studies (with evaluation focusing on known issues and bias, and the potential impact on outcomes, rather than just the risk of bias), we analyze specific outcomes and pooled outcomes (with extensive analysis and validation of pooled outcomes), and we do not include preclinical studies or retracted studies in meta-analysis. Pooled outcomes can detect efficacy earlier but do not bias towards efficacy—the inclusion of less appropriate designs such as very late treatment may hide efficacy for appopriate use.

Preclinical Research

An in silico study supports the efficacy of thermotherapy54.

2 in vitro studies support the efficacy of thermotherapy42,55.

An in vivo animal study supports the efficacy of thermotherapy56.

Preclinical research is an important part of the development of treatments, however results may be very different in clinical trials. Preclinical results are not used in this paper.

Benefits of Fever

Fever is an important component of the acute response to coronavirus infection57. The evolutionary conservation of fever for over 600 million years supports a survival benefit58. Viral particle sensing occurs via pattern recognition receptors, such as toll-like receptors, triggering release of endogenous pyrogens such as interleukin-1. These cytokines induce thermoregulatory centers in the hypothalamus to elevate core temperature setpoints above normal homeostasis. The resulting fever enhances multiple aspects of the innate and adaptive immune systems58, and creates a suboptimal internal environment that impairs SARS-CoV-2 enzyme function and replication. In vitro studies demonstrate reduced viral output at sustained febrile temperatures of 38-39°C compared to basal 37°C conditions. Fever also correlates clinically with heightened interferon-γ, interleukin-6, lymphocyte activation, and antibody production critical for viral clearance.

Los et al. showed that higher temperature enhanced the expression of antiviral genes and reduced SARS-CoV-2 replication in Calu-3 and Caco-2 cells. An in vivo hamster model showed that higher body temperature at the time of infection correlated with lower viral loads.

Zhou et al. showed that SARS-CoV-2 patients with higher fever had lower viral load. Molecular dynamics simulations, surface plasmon resonance experiments, and pseudovirus cell entry assays showed decreased SARS-CoV-2 binding affinity to the human ACE2 receptor at higher temperature (40°C vs. 37°C).

Downing et al. induced hyperthermia (fever-like temperatures) in human volunteers by immersing them in warm water baths. They found that lymphocytes isolated from individuals with core body temperatures elevated to 39°C produced up to 10 times more interferon-γ, as shown in Fig. 4. They also found an increase in suppressor/cytotoxic T cells and natural killer cells. The threshold of 39°C suggests relevance to fever, and the results suggest fever may play a role in boosting antiviral and immunoregulatory activities.

Fig. 4. A 10 fold increase in interferon-γ production was seen when core body temperature reached 39°C, from Downing et al.

Herder et al. perform in vitro analysis with a 3D respiratory epithelial model using cells from human donors. Authors showed that elevated temperature (39-40°C) restricts SARS-CoV-2 infection and replication independently of interferon-mediated antiviral defenses. Authors found SARS-CoV-2 can still enter respiratory cells at 40°C but viral transcription and replication are inhibited, limiting the production of infectious virus. This temperature-dependent restriction correlates with altered host gene expression related to antiviral immunity and epigenetic regulation. The results suggest that febrile temperature ranges may confer protection to respiratory tissues by restricting SARS-CoV-2 propagation.

Dominguez-Nicolas et al. induced localized hyperthermia using LF-ThMS applied to the dorsal thorax (up to 44°C externally), resulting in significantly increased peripheral oxygen saturation (SpO₂) levels in COVID-19 patients, as shown in Fig. 5.

Fig. 5. Rapidly increasing SpO₂ in COVID-19 patients with localized thoracic hyperthermia, from Dominguez-Nicolas et al.

Ramirez et al. compared COVID-19 mortality in Finland and Estonia, where sauna use is part of the culture and is typically practiced at least once a week, with the rest of Europe. Authors found significantly lower mortality with sauna culture, and suggest this may be due to the beneficial effects of hydrothermotherapy.

Ruble et al. compared army hospital vs. sanitarium treatment for the 1918 Spanish influenza, showing lower progression to pneumonia and lower mortality with sanitarium treatment, which involves hydrothermotherapy, sunlight, and fresh air.

Stewart reports on the use of diathermy in the treatment of pneumonia in 1926, with case reports from several physicians covering over 300 patients. Author reports that diathermy had consistent positive effects without significant adverse events, resulted in about half the mortality of the control group, significantly alleviated symptoms such as dyspnea, pain, and cardiac strain, and improved sleep and reduced respiratory rates.

Recent atom-level work strengthens the mechanistic case for fever-mediated viral attenuation. Xie et al. performed 200-ns equilibration followed by replicate 100-ns all-atom MD simulations of the spike RBD-ACE2 peptidase complex across physiologic-to-febrile temperatures. At 315 K the interface lost ~1 hydrogen bond, solvent exposure grew by ~4 Å², dissociation probability tripled, and MM-PBSA binding free energy became ≈59 kcal mol-¹ less favorable, driven by heat-induced straightening of the ACE2 α1-helix and withdrawal of the β3β4 hairpin that jointly destabilise the two anchor regions. Mild-cool conditions (305 K) had the opposite effect, α1-helix curvature tightened the interface, dissociation dropped eight-fold, and binding free energy became ~21 kcal mol-¹ more favorable. These thermodynamic shifts directly support febrile-range hyperthermia as a barrier to initial viral attachment.

In summary, fever is a key component of the response to infection. Fever enhances immune cell performance, induces cellular stress on pathogens, and may act synergistically with other stressors like iron deprivation. While results show beneficial effects of fever, it is not universally beneficial. Extreme or prolonged cases may be harmful. Fever may be more detrimental for individuals with lower tolerance for the increased metabolic demands.

Thermotherapy or heat therapy, which uses various methods for increasing internal body temperature, may have benefits similar to natural fever. Thermotherapy has potential advantages due to localization of treatment, precise temperature control, and lower metabolic cost; and potential risks due to improper application, excessive heat, contraindications, and not fully replicating the complex physiological effects of fever.

Results

Table 1 summarizes the results for all studies, for Randomized Controlled Trials, after exclusions, and for specific outcomes. Fig. 6, 7, 8, 9, 10, 11, 12, and 13 show forest plots for random-effects meta-analysis of all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, progression, recovery, and viral clearance.

Table 1. Random-effects meta-analysis for all studies, for Randomized Controlled Trials, after exclusions, and for specific outcomes. Results show the relative risk with treatment and the 95% confidence interval. * p<0.05 **** p<0.0001.
	Relative Risk	Studies	Patients

All studies	0.44 [0.22‑0.91]*	4	217
After exclusions	0.44 [0.19‑0.99]*	3	195
RCTsRCTs	0.44 [0.19‑0.99]*	3	195

VentilationVent.	0.47 [0.11‑2.02]	2	145
Recovery	0.33 [0.19‑0.57]****	2	90

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October 2026

Thermotherapy COVID-19 studies

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Late treatment56%0.44 · 0.22–0.9115/12320/9456% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.026

All studies56%0.44 · 0.22–0.9115/12320/9456% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.02600.511.52+

Rotate screen for more detailsIncrease width for more details

← Thermotherapy
reduces riskThermotherapy
increases risk →

Fig. 6. Random-effects meta-analysis for all studies. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.

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October 2026

Thermotherapy COVID-19 mortality results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Bonfanti (RCT)−11%1.11 · 0.39–3.194/94/10EXCLUDEDVentilated patients Mancilla-G.. (RCT)43%0.57 · 0.22–1.456/5410/51TherMoCoVheating pad

Late treatment43%0.57 · 0.22–1.456/5410/5143% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.24

All studies43%0.57 · 0.22–1.456/5410/5143% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.2400.511.52+

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← Thermotherapy
reduces riskThermotherapy
increases risk →

Fig. 7. Random-effects meta-analysis for mortality results.

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Thermotherapy COVID-19 mechanical ventilation results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Tian (DB RCT)84%0.16 · 0.02–1.401/273/13diathermy Mancilla-G.. (RCT)21%0.79 · 0.26–2.425/546/51TherMoCoVheating pad

Late treatment53%0.47 · 0.11–2.026/819/6453% lower risk

Tau² = 0.49, I² = 38.8%, p = 0.32

All studies53%0.47 · 0.11–2.026/819/6453% lower risk

Tau² = 0.49, I² = 38.8%, p = 0.3200.511.52+

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← Thermotherapy
reduces riskThermotherapy
increases risk →

Fig. 8. Random-effects meta-analysis for ventilation.

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October 2026

Thermotherapy COVID-19 ICU results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Tian (DB RCT)76%0.24 · 0.05–1.152/274/13diathermy

Late treatment76%0.24 · 0.05–1.152/274/1376% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.074

All studies76%0.24 · 0.05–1.152/274/1376% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.07400.511.52+

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← Thermotherapy
reduces riskThermotherapy
increases risk →

Fig. 9. Random-effects meta-analysis for ICU admission.

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October 2026

Thermotherapy COVID-19 hospitalization results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Huang (RCT)40%0.60 · 0.16–2.25hosp.3/255/25diathermy

Late treatment40%0.60 · 0.16–2.253/255/2540% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.46

All studies40%0.60 · 0.16–2.253/255/2540% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.4600.511.52+

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← Thermotherapy
reduces riskThermotherapy
increases risk →

Fig. 10. Random-effects meta-analysis for hospitalization.

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October 2026

Thermotherapy COVID-19 progression results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Mancilla-G.. (RCT)17%0.83 · 0.45–1.5214/5416/51TherMoCoVheating pad

Late treatment17%0.83 · 0.45–1.5214/5416/5117% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.55

All studies17%0.83 · 0.45–1.5214/5416/5117% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.5500.511.52+

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← Thermotherapy
reduces riskThermotherapy
increases risk →

Fig. 11. Random-effects meta-analysis for progression.

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October 2026

Thermotherapy COVID-19 recovery results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Huang (RCT)67%0.33 · 0.16–0.70no recov.6/2518/25diathermy Tian (DB RCT)67%0.33 · 0.15–0.72no recov.27 (n)13 (n)diathermy

Late treatment67%0.33 · 0.19–0.576/5218/3867% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.0001

All studies67%0.33 · 0.19–0.576/5218/3867% lower risk

Tau² = 0.00, I² = 0.0%, p < 0.000100.511.52+

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← Thermotherapy
reduces riskThermotherapy
increases risk →

Fig. 12. Random-effects meta-analysis for recovery.

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October 2026

Thermotherapy COVID-19 viral clearance results

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Huang (RCT)80%0.20 · 0.01–3.97viral+0/252/25diathermy

Late treatment80%0.20 · 0.01–3.970/252/2580% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.29

All studies80%0.20 · 0.01–3.970/252/2580% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.2900.511.52+

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← Thermotherapy
reduces riskThermotherapy
increases risk →

Fig. 13. Random-effects meta-analysis for viral clearance.

Randomized Controlled Trials (RCTs)

Fig. 14 shows a comparison of results for RCTs and observational studies. Random-effects meta-analysis of RCTs shows 56% improvement, compared to 53% for other studies. Fig. 15 and 16 show forest plots for random-effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. RCT results are included in Table 1.

Fig. 14. Results for RCTs and observational studies.

RCTs have many potential biases

RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases64, and analysis of double-blind RCTs has identified extreme levels of bias65. For COVID-19, the overhead may delay treatment, dramatically compromising efficacy; they may encourage monotherapy for simplicity at the cost of efficacy which may rely on combined or synergistic effects; the participants that sign up may not reflect real world usage or the population that benefits most in terms of age, comorbidities, severity of illness, or other factors; standard of care may be compromised and unable to evolve quickly based on emerging research for new diseases; errors may be made in randomization and medication delivery; and investigators may have hidden agendas or vested interests influencing design, operation, analysis, reporting, and the potential for fraud. All of these biases have been observed with COVID-19 RCTs. There is no guarantee that a specific RCT provides a higher level of evidence.

Conflicts of interest for COVID-19 RCTs

RCTs are expensive and many RCTs are funded by pharmaceutical companies or other organizations with conflicts of interest, for example governments that previously denied treatment with the study drug. For COVID-19, this creates an incentive to show efficacy for patented commercial products, and an incentive to show a lack of efficacy for inexpensive treatments. The bias is expected to be significant, for example Als-Nielsen et al. analyzed 370 RCTs from Cochrane reviews, showing that trials funded by for-profit organizations were 5 times more likely to recommend the experimental drug compared with those funded by nonprofit organizations. Bekelman et al. and Lundh et al. show that industry-sponsored studies are more likely to be favorable. For COVID-19, some major philanthropic organizations are largely funded by investments with extreme conflicts of interest for and against specific COVID-19 interventions.

RCTs for novel acute diseases requiring rapid treatment

High quality RCTs for novel acute diseases are more challenging, with increased ethical issues due to the urgency of treatment, increased risk due to enrollment delays, and more difficult design with a rapidly evolving evidence base. For COVID-19, the most common site of initial infection is the upper respiratory tract. Immediate treatment is likely to be most successful and may prevent or slow progression to other parts of the body. For a non-prophylaxis RCT, it makes sense to provide treatment in advance and instruct patients to use it immediately on symptoms, just as some governments have done by providing medication kits in advance. Unfortunately, no RCTs have been done in this way. Every treatment RCT to date involves delayed treatment. Among the 227 treatments we have analyzed, 67% of RCTs involve very late treatment 5+ days after onset. No non-prophylaxis COVID-19 RCTs match the potential real-world use of early treatments. They may more accurately represent results for treatments that require visiting a medical facility, e.g., those requiring intravenous administration.

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COVID-19 RCT vs. observational results from 6,000+ studies

RR · CI

Low-cost0.98 · 0.90–1.06 High-profit0.93 · 0.85–1.02

All treatments0.97 · 0.91–1.03

00.511.52

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RCTs show
higher efficacyRCTs show
lower efficacy

Fig. 17. For COVID-19, observational study results do not systematically differ from RCTs, RR 0.97 [0.91‑1.03] across 227 treatments69.

Observational studies have been shown to be reliable

Evidence shows that observational studies can also provide reliable results. Concato et al. found that well-designed observational studies do not systematically overestimate the magnitude of the effects of treatment compared to RCTs. Anglemyer et al. analyzed reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates.

We performed a similar analysis across the 227 treatments we cover, showing no significant difference in the results of RCTs compared to observational studies, RR 0.97 [0.91‑1.03]51. Similar results are found for all low-cost treatments, RR 0.98 [0.90‑1.06]. High-cost treatments show a non-significant trend towards RCTs showing greater efficacy, RR 0.93 [0.85‑1.02]. Details can be found in the supplementary data.

Lee et al. showed that only 14% of the guidelines of the Infectious Diseases Society of America were based on RCTs. Evaluation of studies relies on an understanding of the study and potential biases. Limitations in an RCT can outweigh the benefits, for example excessive dosages, excessive treatment delays, or remote survey bias may have a greater effect on results. Ethical issues may also prevent running RCTs for known effective treatments. For more on issues with RCTs see73,74.

RCTs may be less reliable

Concato et al. report a paradoxical finding—RCT results had higher variability, and only RCTs were found to sometimes report significant results the opposite of the overall result. The same trend is seen for the most popular (most politicized) COVID-19 treatments—considering all statistically significant results reported in studies, RCTs are slightly more likely to report a result in the opposite direction. In other words, for these COVID-19 treatments and for the topics covered by Concato et al., assuming causality from a single study is more likely to result in an incorrect conclusion for RCTs.

Increased risk of inconsistent results for RCTs suggests higher prevalence of bias, which may arise due to many issues including design bias, conflicts of interest, treatment differences by physicians aware of allocation, attrition bias, ascertainment bias, randomization failures, errors, or fraud.

Using all studies identifies efficacy 8+ months faster (9+ months for low-cost treatments)

Currently, 59 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. Of these, 54% have been confirmed in RCTs, with a mean delay of 7.8 months (62% with 8.7 months delay for low-cost treatments). The remaining treatments either have no RCTs, or the point estimate is consistent.

All studies must be carefully analyzed

Neither observational studies nor RCTs prove causation—any study can be flawed or fraudulent. We need much more, for example a combination of results from many independent teams, detailed understanding of each study, knowledge of conflicts/team reliability, dose-response relationships, delay-response relationships, logical results across outcomes, or details consistent with preclinical expectations.

All studies must be evaluated individually. RCTs for a given medication and disease may be more reliable, however they may also be less reliable. For off-patent medications, very high conflict of interest trials may be more likely to be RCTs, and more likely to be large trials that dominate meta-analyses.

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Thermotherapy COVID-19 Randomized Controlled Trials

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Huang (RCT)80%0.20 · 0.01–3.97oxygen0/252/25diathermy Tian (DB RCT)84%0.16 · 0.02–1.40ventilation1/273/13diathermy Bonfanti (RCT)−11%1.11 · 0.39–3.19death4/94/10EXCLUDEDVentilated patients Mancilla-G.. (RCT)43%0.57 · 0.22–1.45death6/5410/51TherMoCoVheating pad

Late treatment56%0.44 · 0.19–0.997/10615/8956% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.048

All studies56%0.44 · 0.19–0.997/10615/8956% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.04800.511.52+

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← Thermotherapy
reduces riskThermotherapy
increases risk →

Fig. 15. Random-effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.

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Thermotherapy COVID-19 RCT mortality results

StudyImprovementRR · 95% CITreatmentControlRelative Risk

Bonfanti (RCT)−11%1.11 · 0.39–3.194/94/10EXCLUDEDVentilated patients Mancilla-G.. (RCT)43%0.57 · 0.22–1.456/5410/51TherMoCoVheating pad

Late treatment43%0.57 · 0.22–1.456/5410/5143% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.24

All studies43%0.57 · 0.22–1.456/5410/5143% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.2400.511.52+

Rotate screen for more detailsIncrease width for more details

← Thermotherapy
reduces riskThermotherapy
increases risk →

Fig. 16. Random-effects meta-analysis for RCT mortality results.

Exclusions

We exclude studies with mechanically ventilated patients because thermotherapy is typically recommended earlier in infection where the mechanisms of action are expected to be more relevant.

To avoid bias in the selection of studies, we analyze all other non-retracted studies. Here we show the results after excluding studies with major issues likely to alter results, non-standard studies, and studies where very minimal detail is currently available. Our bias evaluation is based on analysis of each study and identifying when there is a significant chance that limitations will substantially change the outcome of the study. We believe this can be more valuable than checklist-based approaches such as Cochrane GRADE, which can be easily influenced by potential bias, may ignore or underemphasize serious issues not captured in the checklists, and may overemphasize issues unlikely to alter outcomes in specific cases (for example certain specifics of randomization with a very large effect size and well-matched baseline characteristics).

The studies excluded are as below. Fig. 18 shows a forest plot for random-effects meta-analysis of all studies after exclusions.

Dominguez-Nicolas, the study design does not provide a clear relative risk.

c19early.org

October 2026

Thermotherapy COVID-19 studies after exclusions

StudyImprovementRR · 95% CIOutcomeTreatmentControlRelative Risk

Huang (RCT)80%0.20 · 0.01–3.97oxygen0/252/25diathermy Tian (DB RCT)84%0.16 · 0.02–1.40ventilation1/273/13diathermy Mancilla-G.. (RCT)43%0.57 · 0.22–1.45death6/5410/51TherMoCoVheating pad

Late treatment56%0.44 · 0.19–0.997/10615/8956% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.048

All studies56%0.44 · 0.19–0.997/10615/8956% lower risk

Tau² = 0.00, I² = 0.0%, p = 0.04800.511.52+

Rotate screen for more detailsIncrease width for more details

← Thermotherapy
reduces riskThermotherapy
increases risk →

Fig. 18. Random-effects meta-analysis for all studies after exclusions. This plot shows pooled effects, see the specific outcome analyses for individual outcomes. Analysis validating pooled outcomes for COVID-19 can be found below. Effect extraction is pre-specified, using the most serious outcome reported. For details see the appendix.

Media Censorship

Low-cost treatments were subject to bias and censorship during the pandemic. Scientific bias is seen in the design, analysis, presentation, and selective reporting of studies, which often favored negative results. A similar bias is seen in the media coverage for low-cost treatments. While broadly seen, bias was particularly notable for ivermectin and hydroxychloroquine, e.g., Scott Alexander noted that "if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."47.

We analyze media coverage for the 227 treatments we cover using Altmetric75, which reports the number of ~12,000 tracked news outlets that covered each study76. Studies are considered to have received significant media coverage if they were covered by at least 0.5% of the tracked news outlets. Fig. 19, 20, and 21 show the bias toward negative results for low-cost treatments, in contrast to the opposite bias for high-profit treatments. This may result in widespread incorrect perceptions on the relative efficacy of high-profit and low-cost treatments. The impact is significant—increased cost limits the use of high-profit treatments and treatment equity, and high-profit treatments were also more difficult to access, especially for earlier treatment which improves efficacy and minimizes community transmission.

The mainstream media did not cover any of the positive studies for thermotherapy.

Fig. 19. Media was more likely to cover negative or inconclusive results for low-cost treatments, and positive results for high-profit treatments.

Fig. 20. Mainstream media was biased against positive results for low-cost treatments.

Fig. 21. In contrast to the results for low-cost treatments, mainstream media was biased towards positive results for high-cost treatments.

A combination of factors may have led to the media's suppression of low-cost treatments:

•

Politicization

led to a media environment where coverage was often framed to support a political narrative rather than to provide objective scientific information. As Scott Alexander said:

"if you say anything in favor of ivermectin you will be cast out of civilization and thrown into the circle of social hell reserved for Klan members and 1/6 insurrectionists. All the health officials in the world will shout 'horse dewormer!' at you and compare you to Josef Mengele."

There was strong social pressure to discredit low-cost treatments.

•

Censorship

of information conflicting with selected authorities. For example, individuals and organizations presenting conflicting science were often banned on Twitter and YouTube.

•

FDA requires "no adequate, approved, and available alternatives"

in order to grant an EUA for novel high-profit interventions, creating a strong incentive for authorities to ignore or downplay existing low-cost treatments.

•

Regulatory capture

biases authorities towards high-profit interventions.

•

Authorities ignored most evidence for low-cost treatments

, for example the NIH references only 2% of studies in delayed, rarely-updated, biased commentaries with no quantitive analysis.

•

Media coverage of science is often not very accurate

, e.g., misunderstanding confounding issues. For example the media widely considered the RECOVERY HCQ RCT to be conclusive on efficacy, but very late treatment of late stage patients (mostly on oxygen already) with an excessive toxic dose (shown dangerous in a dose comparison RCT) provides no information on the recommended early/prophylactic treatment. With difficulting in understanding basic confounders like treatment delay and dose, the media may favor deferring to authorities. Many studies for low-cost treatments require greater expertise to analyze. Relatively few journalists have a strong ability to analyze clinical trials and are outnumbered by the rest.

•

Substantial funding from pharmaceutical advertising

biases editorial decisions towards high-profit interventions.

•

PR power

- companies/teams with strong PR presence are favored in the media, which correlates with high-profit and high conflict of interest studies.

•

The media was very negative in general

, inflating risk, fear, and anxieties. A negative bias may improve ratings and revenue, increasing motivation to continue watching coverage. A combination of low-cost treatments greatly reducing risk conflicts with the negative narrative.

Authority Delay

25 low-cost treatments were approved in one or more countries, yet many countries approved no low-cost treatments. The countries that did adopt low-cost treatments analyzed the evidence early and made timely approvals. With few exceptions, authorities did not change their initial views, regardless of how much evidence accumulated showing either efficacy or harm. Why?

The harms of smoking here hidden for 25 yearsC. Authorities did not analyze the data in real-time, failing to act when harm was known. Widespread acknowledgement of harm came only after attempts by two new surgeon generals, along with pressure from health advocates and a new president, and a review of 7,000 studies.

Similarly for COVID-19, most authorities and experts did not proactively analyze data in real-time. This guarantees delayed recognition of efficacy or harm, by which time moral, legal, career, and reputational liabilities strongly disincentivize any admission of error. Claims of no efficacy (for effective treatments) or safety (for harmful treatments) were often made prior to strong data being available. Correction would require admitting to errors that increased mortality, which is unlikely with the same generation of officials.

Analysis of potential treatments was rarely done, and when done these were typically minimal efforts. For example, NIH reviews were highly delayed, cover only a tiny fraction of treatments, reference only 2% of studies for the treatments covered, and include no quantitative analysis. They appear as rarely updated side projects from external panels implicitly tasked with justifying prior failures. As with smoking, the thousands of studies could (and should) have been analyzed and acted on in real-time.

A key structural improvement, applicable to all current and future diseases, is for authorities to implement real-time proactive analysis of clinical evidence. This does not remove all bias, but does make it possible to act on evidence, whereas delayed action may be unlikely due to moral, legal, career, and reputational liabilities.

c19early.org

Delayed public health acknowledgments

Official acknowledgment of efficacy or harm is often delayed—legal, career, and status risks disincentivize admission of error.

	Evidence	Official Acknowledgment	Approx. Delay
Citrus Fruit (vitamin C) for Scurvy (effectiveness)	1747: James Lind conducted one of the first-ever controlled clinical trials, proving that oranges and lemons cured scurvy in sailors.	1795: The British Royal Navy finally made a daily ration of lemon juice a standard issue for all its sailors, effectively eliminating the disease.	48 years
Handwashing (lower mortality)	1847: Dr. Ignaz Semmelweis provided conclusive proof that having doctors wash their hands with a chlorine solution before delivering babies reduced maternal mortality rates from over 18% to around 1%.	~1870s: Semmelweis's findings were rejected and he was ridiculed. His work was only validated decades later (after his death).	~20+ years
Helicobacter pylori (bacteria causes ulcers)	1982-1984: Marshall and Warren discovered that Helicobacter pylori bacteria causes ulcers, confirmed via direct exposure. Officials maintained that ulcers were caused by stress and spicy food.	1994: The US NIH released a consensus statement officially recommending antibiotics as the standard treatment for peptic ulcers, overturning decades of acid-suppression therapy.	~12 years
Asbestos (causes asbestosis & cancer)	1924: The British Medical Journal published the first case study of a death from "asbestosis." By 1918, U.S. insurance companies had stopped selling life insurance to asbestos workers.	1971 (US): The Occupational Safety and Health Administration (OSHA) was formed and began regulating asbestos as a carcinogen, setting the first federal workplace safety standards for it.	~47 years
Leaded Gasoline (neurotoxicity)	~1924: Dangers of low-level lead exposure were known. Experts like Alice Hamilton warned the U.S. Surgeon General that adding lead to gasoline would cause widespread public poisoning.	1973 (US): The Environmental Protection Agency (EPA) ordered the first phasedown of lead in gasoline, following the Clean Air Act of 1970. A full ban for on-road vehicles took effect in 1996.	~49 years
Harms of Smoking (causes lung cancer)	1939: Franz Müller (Germany) published the first case-control epidemiological study strongly linking tobacco smoking to lung cancer. This was followed by major U.S. & U.K. studies in the 1950s.	1964 (US): The U.S. Surgeon General's report, "Smoking and Health," was released. It was the first U.S. government report to definitively link smoking to lung cancer and heart disease.	25 years

Heterogeneity

Heterogeneity in COVID-19 studies arises from many factors including:

Treatment delay

The time between infection or the onset of symptoms and treatment may critically affect how well a treatment works. For example an antiviral may be very effective when used early but may not be effective in late stage disease, and may even be harmful. Oseltamivir, for example, is generally only considered effective for influenza when used within 0-36 or 0-48 hours77,78. Baloxavir marboxil studies for influenza also show that treatment delay is critical — Ikematsu et al. report an 86% reduction in cases for post-exposure prophylaxis, Hayden et al. show a 33 hour reduction in the time to alleviation of symptoms for treatment within 24 hours and a reduction of 13 hours for treatment within 24-48 hours, and Kumar et al. report only 2.5 hours improvement for inpatient treatment.

Table 2. Studies of baloxavir marboxil for influenza show that early treatment is more effective.
Treatment delay	Result
Post-exposure prophylaxis	86% fewer cases79
<24 hours	-33 hours symptoms80
24-48 hours	-13 hours symptoms80
Inpatients	-2.5 hours to improvement81

Fig. 22 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 227 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.

Fig. 22. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 227 treatments.

Patient demographics

Details of the patient population including age and comorbidities may critically affect how well a treatment works. For example, many COVID-19 studies with relatively young low-comorbidity patients show all patients recovering quickly with or without treatment. In such cases, there is little room for an effective treatment to improve results, for example as in López-Medina et al.

SARS-CoV-2 variants

Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants83, for example the Gamma variant shows significantly different characteristics84-87. Different mechanisms of action may be more or less effective depending on variants, for example the degree to which TMPRSS2 contributes to viral entry can differ across variants88,89.

Treatment regimen

Effectiveness may depend strongly on the dosage and treatment regimen.

Medication quality

The quality of medications may vary significantly between manufacturers and production batches, which may significantly affect efficacy and safety. Williams et al. analyze ivermectin from 11 different sources, showing highly variable antiparasitic efficacy across different manufacturers. Xu et al. analyze a treatment from two different manufacturers, showing 9 different impurities, with significantly different concentrations for each manufacturer.

Other treatments

The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic92-116, therefore efficacy may depend strongly on combined treatments.

Effect measured

Across all studies there is a strong association between different outcomes, for example improved recovery is strongly associated with lower mortality. However, efficacy may differ depending on the effect measured, for example a treatment may be more effective against secondary complications and have minimal effect on viral clearance.

Meta-analysis

The distribution of studies will alter the outcome of a meta-analysis. Consider a simplified example where everything is equal except for the treatment delay, and effectiveness decreases to zero or below with increasing delay. If there are many studies using very late treatment, the outcome may be negative, even though early treatment is very effective. All meta-analyses combine heterogeneous studies, varying in population, variants, and potentially all factors above, and therefore may obscure efficacy by including studies where treatment is less effective. Generally, we expect the estimated effect size from meta-analysis to be less than that for the optimal case. Looking at all studies is valuable for providing an overview of all research, important to avoid cherry-picking, and informative when a positive result is found despite combining less-optimal situations. However, the resulting estimate does not apply to specific cases such as early treatment in high-risk populations. While we present results for all studies, we also present treatment time and individual outcome analyses, which may be more informative for specific use cases.

Pooled Effects

Combining studies is required

For COVID-19, delay in clinical results translates into additional death and morbidity, as well as additional economic and societal damage. Combining the results of studies reporting different outcomes is required. There may be no mortality in a trial with low-risk patients, however a reduction in severity or improved viral clearance may translate into lower mortality in a high-risk population. Different studies may report lower severity, improved recovery, and lower mortality, and the significance may be very high when combining the results. "The studies reported different outcomes" is not a good reason for disregarding results. Pooling the results of studies reporting different outcomes allows us to use more of the available information. Logically we should, and do, use additional information when evaluating treatments—for example dose-response and treatment delay-response relationships provide additional evidence of efficacy that is considered when reviewing the evidence for a treatment.

Specific outcome and pooled analyses

We present both specific outcome and pooled analyses. In order to combine the results of studies reporting different outcomes we use the most serious outcome reported in each study, based on the thesis that improvement in the most serious outcome provides comparable measures of efficacy for a treatment. A critical advantage of this approach is simplicity and transparency. There are many other ways to combine evidence for different outcomes, along with additional evidence such as dose-response relationships, however these increase complexity.

Ethical and practical issues limit high-risk trials

Trials with high-risk patients may be restricted due to ethics for treatments that are known or expected to be effective, and they increase difficulty for recruiting. Using less severe outcomes as a proxy for more serious outcomes allows faster and safer collection of evidence.

Validating pooled outcome analysis for COVID-19

For many COVID-19 treatments, a reduction in mortality logically follows from a reduction in hospitalization, which follows from a reduction in symptomatic cases, which follows from a reduction in PCR positivity. We can directly test this for COVID-19.

Analysis of the the association between different outcomes across studies from all 227 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Fig. 23 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.0000000001). Similarly, Fig. 24 shows that improved recovery is very strongly associated with lower mortality (p < 0.0000000001). Considering the extremes, Singh et al. show an association between viral clearance and hospitalization or death, with p = 0.003 after excluding one large outlier from a mutagenic treatment, and based on 44 RCTs including 52,384 patients. Fig. 25 shows that improved viral clearance is strongly associated with fewer serious outcomes. The association is very similar to Singh et al., with higher confidence due to the larger number of studies. As with Singh et al., the confidence increases when excluding the outlier treatment, from p = 0.0000000074 to p = 0.00000000015.

Fig. 23. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.

Fig. 24. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.

Fig. 23. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.

Pooled outcomes identify efficacy 5 months faster (7 months for RCTs)

Currently, 59 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 85% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.6 months. When restricting to RCTs only, 53% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 7.5 months. Fig. 26 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.

Fig. 26. The time when studies showed that treatments were effective, defined as statistically significant improvement of ≥10% from ≥3 studies. Pooled results typically show efficacy earlier than specific outcome results. Results from all studies often shows efficacy much earlier than when restricting to RCTs. Results reflect conditions as used in trials to date, these depend on the population treated, treatment delay, and treatment regimen.

Limitations

Pooled analysis could hide efficacy, for example a treatment that is beneficial for late stage patients but has no effect on viral clearance may show no efficacy if most studies only examine viral clearance. In practice, it is rare for a non-antiviral treatment to report viral clearance and to not report clinical outcomes; and in practice other sources of heterogeneity such as differences in treatment delay are more likely to hide efficacy.

Summary

Analysis validates the use of pooled effects and shows significantly faster detection of efficacy on average. However, as with all meta-analyses, it is important to review the different studies included. We also present individual outcome analyses, which may be more informative for specific use cases.

Discussion

Thermotherapy

Thermotherapy, or heat therapy includes hydrothermotherapy, hydrotherapy, and diathermy, methods for increasing internal body temperature which may have benefits similar to natural fever. Thermotherapy has potential advantages over natural fever: treatment can be localized to specific tissues or regions, the temperature can be precisely controlled, and it may greatly reduce the metabolic cost and potential for tissue damage compared with more systemic fever. However, thermotherapy may not fully replicate the complex physiological effects of fever, and may also carry risks - improper application or excessive heat may lead to burns, dehydration, or heat-induced injuries. Thermotherapy may be contraindicated with certain medical conditions, for example when increased blood flow poses a risk.

Results for other infections

Studies have also shown efficacy with thermotherapy for pneumonia43, the common cold44, SARS-CoV-145, and influenza46.

Publication bias

Publishing is often biased towards positive results, however evidence suggests that there may be a negative bias for inexpensive treatments for COVID-19. Both negative and positive results are very important for COVID-19, media in many countries prioritizes negative results for inexpensive treatments (inverting the typical incentive for scientists that value media recognition), and there are many reports of difficulty publishing positive results118-121. For thermotherapy, there is currently not enough data to evaluate publication bias with high confidence.

Limitations

Summary statistics from meta-analysis necessarily lose information. As with all meta-analyses, studies are heterogeneous, with differences in treatment delay, treatment regimen, patient demographics, variants, conflicts of interest, standard of care, and other factors. We provide analyses for specific outcomes and by treatment delay, and we aim to identify key characteristics in the forest plots and summaries. Results should be viewed in the context of study characteristics.

Some analyses classify treatment based on early or late administration, as done here, while others distinguish between mild, moderate, and severe cases. Viral load does not indicate degree of symptoms — for example patients may have a high viral load while being asymptomatic. With regard to treatments that have antiviral properties, timing of treatment is critical — late administration may be less helpful regardless of severity.

Details of treatment delay per patient is often not available. For example, a study may treat 90% of patients relatively early, but the events driving the outcome may come from 10% of patients treated very late. Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.

Comparison across treatments is confounded by differences in the studies performed, for example dose, variants, and conflicts of interest. Trials with conflicts of interest may use designs better suited to the preferred outcome.

In some cases, the most serious outcome has very few events, resulting in lower confidence results being used in pooled analysis, however the method is simpler and more transparent. This is less critical as the number of studies increases. Restriction to outcomes with sufficient power may be beneficial in pooled analysis and improve accuracy when there are few studies, however we maintain our pre-specified method to avoid any retrospective changes.

Studies show that combinations of treatments can be highly synergistic and may result in many times greater efficacy than individual treatments alone92-116. Therefore standard of care may be critical and benefits may diminish or disappear if standard of care does not include certain treatments.

This real-time analysis is constantly updated based on submissions. Accuracy benefits from widespread review and submission of updates and corrections from reviewers. Less popular treatments may receive fewer reviews.

No treatment or intervention is 100% available and effective for all current and future variants. Efficacy may vary significantly with different variants and within different populations. All treatments have potential side effects. Propensity to experience side effects may be predicted in advance by qualified physicians. We do not provide medical advice. Before taking any medication, consult a qualified physician who can compare all options, provide personalized advice, and provide details of risks and benefits based on individual medical history and situations.

Notes

Currently all studies are peer-reviewed. Thermotherapy methods may have additional mechanisms of action beyond increased internal body temperatures. Studies of ventilated patients are excluded1. Dominguez-Nicolas et al. is included in the main analysis, however the weight is limited. While providing significant evidence of benefit, the study does not provide a clear relative risk.

Reviews

Many reviews cover thermotherapy for COVID-19, presenting additional background on mechanisms and related results, including58,62,122-126.

Perspective

Results compared with other treatments

SARS-CoV-2 infection and replication involves a complex interplay of 500+ host and viral proteins and other factors32-39, providing many therapeutic targets. Over 12,000 compounds have been predicted to reduce COVID-19 risk40, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Thermotherapy is known to modulate the immune system41 and to minimize SARS-CoV-2 replication42. Fig. 27 shows an overview of the results for thermotherapy in the context of multiple COVID-19 treatments, and Fig. 28 shows a plot of efficacy vs. cost for COVID-19 treatments.

Fig. 27. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random-effects meta-analysis. 0.5% of 12,000+ proposed treatments show efficacy127.

Fig. 28. Efficacy vs. cost for COVID-19 treatments.

Conclusion

Thermotherapy, or heat therapy includes hydrothermotherapy, hydrotherapy, and diathermy, methods for increasing internal body temperature which may have benefits similar to natural fever, while providing potential advantages regarding localization, precision, and lower metabolic cost. Thermotherapy is known to modulate the immune system41 and to minimize SARS-CoV-2 replication42.

Studies to date show that thermotherapy is an effective treatment for COVID-19. Significantly lower risk is seen for recovery. 3 studies from 3 independent teams in 2 countries show significant benefit. Meta-analysis using the most serious outcome reported shows 56% [9‑78%] lower risk. Results are similar for Randomized Controlled Trials and higher quality studies.

Currently there is limited data, with only 217 patients and only 20 control events for the most serious outcome in trials to date.

Studies have also shown efficacy with thermotherapy for pneumonia43, the common cold44, SARS-CoV-145, and influenza46.

Thermotherapy methods may have additional mechanisms of action beyond increased internal body temperatures. Studies of ventilated patients are excluded1. Dominguez-Nicolas et al. is included in the main analysis, however the weight is limited. While providing significant evidence of benefit, the study does not provide a clear relative risk.

Contact.

Funding.

We have received no funding or compensation in any form, and do not accept donations. This is entirely volunteer work.

Conflicts of interest.

We have no conflicts of interest. We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.

AI.

We use AI models (Gemini, Grok, Claude, and ChatGPT) tasked with functioning as additional peer-reviewers to check for errors, suggest improvements, and review spelling and grammar. Any corrections are manually verified. Our preference for em dashes is independent of AI.

Updates.

Our COVID-19 meta-analyses involve the extraction of over 228,000 datapoints from thousands of papers for 227 treatments. We thank the thousands of scientists, physicians, and other contributors that have provided updates, suggestions, feedback, and corrections. These are all welcome and can be submitted at https://c19early.org/ttmeta.html.

Dedication.

This work is dedicated to top evidence-based physicians that worked tirelessly to analyze evidence and greatly reduce mortality and morbidity during the pandemic. In alphabetical order: Dr. Thomas J. Borody, Dr. Mary Talley Bowden, Dr. Flavio Cadegiani, Dr. Shankara Chetty, Dr. Ryan Cole, Dr. George Fareed, Dr. Sabine Hazan, Dr. Pierre Kory, Dr. Tess Lawrie, Dr. Robert Malone, Dr. Paul Marik, Dr. Peter McCullough, Dr. Didier Raoult, Dr. Harvey Risch, Dr. Jackie Stone, Dr. Brian Tyson, Dr. Joseph Varon, and Dr. Vladimir Zelenko.

Public domain.

This is a public domain work distributed in accordance with the Creative Commons CC0 1.0 Universal license, which dedicates the work to the public domain by waiving all rights worldwide under copyright law. You can distribute, remix, adapt, and build upon this work in any medium or format, including for commercial purposes, without asking permission. Referenced material and third-party images retain any original copyrights or restrictions. See: https://creativecommons.org/publicdomain/zero/1.0/.

Study Notes

Dominguez-Nicolas

Outcome

Improvement

Relative Risk · 95% CI

Improvement in SpO2 <5

53%0.47 · 0.22–1.01

Improvement in SpO2

93%0.07 · 0.01–0.48

Improvement in SpO2 <2

93%0.07 · 0.01–0.48

Improvement in SpO2 <3

96%0.04 · 0.00–0.68

Improvement in SpO2 <4

82%0.18 · 0.06–0.49

Improvement in SpO2 <5 (b)

53%0.47 · 0.22–1.01

Improvement in SpO2 <6

35%0.65 · 0.30–1.39

Improvement in SpO2 <7

35%0.65 · 0.30–1.39

Improvement in SpO2 <8

18%0.82 · 0.66–1.03

Improvement in SpO2 <9

18%0.82 · 0.66–1.03

Improvement in SpO2 <10

12%0.88 · 0.74–1.05

Improvement in SpO2 <11

6%0.94 · 0.84–1.06

00.511.52+

← Thermotherapy
reduces riskThermotherapy
increases risk →

Greater improvement with thermotherapy

Not statistically significant · p = 0.054

Single-blind, sham-controlled, crossover study of 17 COVID-19 outpatients showing significantly increased peripheral oxygen saturation (SpO2) levels correlated with hyperthermia (up to 44°C) produced by 30 minutes of low-field thoracic magnetic stimulation (LF-ThMS) applied to the dorsal thorax. The safety and lack of adverse events supports future research into mechanisms and potential therapeutic use of localized heat therapy for improving respiratory function in COVID-19 patients. Submit Corrections or Updates.

Huang

Outcome

Improvement

Relative Risk · 95% CI

Viral clearance, day 28

80%0.20 · 0.01–3.97

Viral clearance, day 21

50%0.50 · 0.14–1.78

Viral clearance, day 14

-57%1.57 · 0.73–3.39

Viral clearance, day 7

-28%1.28 · 0.98–1.67

00.511.52+

← Thermotherapy
reduces riskThermotherapy
increases risk →

Improved recovery with thermotherapy

p = 0.0016

RCT 50 hospitalized COVID-19 pneumonia patients showing faster recovery with ultra-short wave diathermy (USWD). The USWD group received standard treatment plus USWD applied to the chest for 10 minutes twice daily for 12 days. The USWD group had significantly faster clinical recovery by 6.7 days, lower systemic inflammation, and better outcomes on the 7-point clinical status scale on days 21 and 28 compared to the control group receiving only standard treatment. There was no significant difference in SARS-CoV-2 viral clearance. Pulmonary fibrosis observed prior to treatment was recovered in most patients in both groups, alleviating concerns over potential harms of USWD.

Baseline severe cases were more common in the treatment group, 52 vs. 28%. Submit Corrections or Updates.

Mancilla-Galindo

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Thermotherapy
reduces riskThermotherapy
increases risk →

Lower mortality with thermotherapy

Not statistically significant · p = 0.28

RCT 105 hospitalized patients with mild-to-moderate COVID-19, evaluating the efficacy and safety of local thermotherapy (heating pads applied to the chest for 90 minutes twice daily for 5 days) to prevent disease progression, compared to standard care alone. The thermotherapy was well-tolerated with no significant adverse events.

Reduction in NEWS-2 score was significantly faster with treatment. There was lower progression and mortality with treatment, without statistical significance. The study was underpowered due to early termination.

The temperature used may be too low. Lung temperature is expected to be lower than the external skin surface temperature measured on the thorax, due to heat diffusion and dissipation that occurs in transferring thermal energy across the tissue layers of skin, adipose, muscle, connective tissue and bone between the heating pad and the lung.

The treatment group had greater severity at baseline, NEWS-2 7 vs. 5, and PH-COVID-19 high-risk 7.5% vs. 0%.

Mortality numbers do not match - Figure 3 shows 10 control deaths at 28 days, while Table 3 shows 8. Percentages reported in Table 3 do not match the counts.

ICU numbers do not match the other data, for example in the control group 6 patients required invasive mechanical ventilation and 10 patients died, but only 3 patients were admitted to the ICU. Submit Corrections or Updates.

Tian

Outcome

Improvement

Relative Risk · 95% CI

00.511.52+

← Diathermy
reduces riskDiathermy
increases risk →

Improved recovery with diathermy

p = 0.005

RCT 42 moderate COVID-19 inpatients showing significantly faster clinical and CT scan improvement with short-wave diathermy (SWD) treatment added to standard care, compared to placebo SWD plus standard care. 92.6% of the SWD group had clinical improvement at 14 days, compared to 69.2% in the control group. The SWD group also had significantly faster CT scan improvement. There was no significant difference in adverse events between groups, with only minor side effects like headache and dizziness reported. Submit Corrections or Updates.

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Appendix 1. Methods and Data

Search methods

We perform ongoing searches of PubMed, medRxiv, Europe PMC, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.org, which regularly receives notification of studies upon publication. Search terms are thermotherapy and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion. All studies regarding the use of thermotherapy for COVID-19 that report a comparison with a control group are included in the main analysis. Sensitivity analysis is performed, excluding studies with major issues, epidemiological studies, and studies with minimal available information. Studies with major unexplained data issues, for example major outcome data that is impossible to be correct with no response from the authors, are excluded.

Fig. 29. Mid-recovery results can more accurately reflect efficacy when almost all patients recover. Mateja et al. confirm that intermediate viral load results more accurately reflect hospitalization/death.

Effect extraction

We extracted effect sizes and associated data from all studies. If studies report multiple kinds of effects then the most serious outcome is used in pooled analysis, while other outcomes are included in the outcome-specific analyses. For example, if effects for mortality and cases are reported then they are both used in specific outcome analyses, while mortality is used for pooled analysis. If symptomatic results are reported at multiple times, we use the latest time, for example if mortality results are provided at 14 days and 28 days, the results at 28 days have preference. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms are not used, the next most serious outcome with one or more events is used. For example, in low-risk populations with no mortality, a reduction in mortality with treatment is not possible, however a reduction in hospitalization, for example, is still valuable. Clinical outcomes are considered more important than viral outcomes. When basically all patients recover in both treatment and control groups, preference for viral clearance and recovery is given to results mid-recovery where available. After most or all patients have recovered there is little or no room for an effective treatment to do better, however faster recovery is valuable. An IPD meta-analysis confirms that intermediate viral load reduction is more closely associated with hospitalization/death than later viral load reduction128. If only individual symptom data is available, the most serious symptom has priority, for example difficulty breathing or low SpO₂ is more important than cough.

Statistical methods

Forest plots are computed using PythonMeta129 with the DerSimonian and Laird random-effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Results are presented with 95% confidence intervals. Heterogeneity among studies was assessed using the I² statistic. When results provide an odds ratio, we compute the relative risk when possible, or convert to a relative risk according to Zhang (B) et al. Reported confidence intervals and p-values are used when available, and adjusted values are used when provided. If multiple types of adjustments are reported propensity score matching and multivariable regression has preference over propensity score matching or weighting, which has preference over multivariable regression. Adjusted results have preference over unadjusted results for a more serious outcome when the adjustments significantly alter results. When needed, conversion between reported p-values and confidence intervals followed Altman, Altman (B), and Fisher's exact test was used to calculate p-values for event data. If continuity correction for zero values is required, we use the reciprocal of the opposite arm with the sum of the correction factors equal to 1133. Results are expressed with RR < 1.0 favoring treatment, and using the risk of a negative outcome when applicable (for example, the risk of death rather than the risk of survival). If studies only report relative continuous values such as relative times, the ratio of the time for the treatment group versus the time for the control group is used. Calculations are done in Python (3.14.8) with scipy (1.18.1), pythonmeta (1.26), numpy (2.5.3), statsmodels (0.15.0), and plotly (6.9.0). Mixed-effects meta-regression results are computed with R (4.4.0) using the metafor (4.6-0) and rms (6.8-0) packages, and using the most serious sufficiently powered outcome. For all statistical tests, a p-value less than 0.05 was considered statistically significant. Grobid 0.8.2 is used to parse PDF documents.

When evaluating potential effect modification across groups, we use an interaction test as described by Altman (C) et al. We compared the log-transformed relative risks using a z-test, deriving the standard error of the difference from the 95% confidence intervals. A two-sided interaction p-value of < 0.05 was considered a statistically significant difference in treatment effect between the groups.

Quality evaluation

Cochrane RoB 2/ROBINS-I are often used to evaluate studies, and have the advantage of providing standardized rules that can be applied with minimal understanding of the domain and study. However, the rules do not account for many real-world issues, often overemphasize or underemphasize others, and studies show low inter-rater reliability141. Certain domains are more applicable for these tools, however the time-sensitive nature of a pandemic, with significant mortality for every day of delay in evidence assessment, and the characteristics of COVID-19 make them inappropriate for this domain. This can be demonstrated with examples where expert RoB 2/ROBINS-I ratings do not match reality for COVID-19. Popp et al. use RoB 2 to classify Reis et al. as low risk of bias, however this is the opposite of reality—the trial not only has very high risk of bias, but has very high actual known bias, refusing to release data despite pledging to, reporting multiple impossible numbers, having blinding and randomization failure, and many other issues143. Axfors et al. use RoB 2 to classify Horby et al. as low risk of bias, however this is the opposite of reality—the very late treatment and excessive dosage used produces results with no relevance to recommended usage. HCQ shows poor results with late treatment and excessive dosage, and the combination shows harmD. Hempenius et al. use ROBINS-I to classify 33 studies for HCQ. The two rated as having the lowest risk of bias139,140 are far from the most informative. Both involve very late treatment, providing no information on recommended usage, and ROBINS-I does a very poor job of accounting for the impact of confounding factorsE.

Our quality evaluation focuses on known issues and bias, and the potential impact on outcomes, rather than just the risk of bias. The estimated potential impact of each confounding factor, and the direction of the impact is considered. For example, consider a study that shows significantly lower risk, the value of the study varies significantly if confounding points to an underestimate or an overestimate of efficacy. In one case, the real effect may be null, while the other case provides stronger evidence of efficacy (which may be greater than the study shows). Analysis focusing on the risk of bias, while simpler, may penalize studies for theoretical or technical issues that have no or minimal impact on outcomes. Analysis also depends on the outcome, for example certain issues are less relevant for objective outcomes such as mortality. Inaccurate penalization, and inaccurate high-quality evaluation in the face of known major issues affecting outcomes, increases in significance during a pandemic when immediate recognition of new evidence is critical, and when considering all global studies, as required during a pandemic. Investigators in other countries may have different customs for design, analysis, and reporting, and different English language skills, however they may not be less diligent or have greater bias. Investigators in lower-pharmaceutical-profit countries may have lower bias towards profitable interventions.

Treatment time

We have classified studies as early treatment if most patients are not already at a severe stage at the time of treatment (for example based on oxygen status or lung involvement), and treatment started within 5 days of the onset of symptoms. If studies contain a mix of early treatment and late treatment patients, we consider the treatment time of patients contributing most to the events (for example, consider a study where most patients are treated early but late treatment patients are included, and all mortality events were observed with late treatment patients). We note that a shorter time may be preferable. Antivirals are typically only considered effective when used within a shorter timeframe, for example 0-36 or 0-48 hours for oseltamivir, with longer delays not being effective77,78.

Living analysis

This is a living analysis and is updated regularly. Submit updates or corrections with the form below. We received no funding, this research is done in our spare time. We have no affiliation with any pharmaceutical companies, supplement companies, governments, political parties, or advocacy organizations.

A summary of study results is below. Please submit updates and corrections at the bottom of this page.

A summary of study results is below. Please submit updates and corrections at https://c19early.org/ttmeta.html.

Late treatment

Effect extraction follows pre-specified rules as detailed above and gives priority to more serious outcomes. For pooled analyses, the first (most serious) outcome is used, which may differ from the effect a paper focuses on. Other outcomes are used in outcome specific analyses.

Bonfanti, 11/30/2023, Randomized Controlled Trial, USA, peer-reviewed, mean age 60.5, 8 authors, study period September 2020 - February 2022, average treatment delay 9.4 days, trial NCT04494867 (history), excluded: very late treatment, mechanically ventilated patients, baseline SOFA and PaO2/FiO2 show higher severity in the treatment group; very late stage, ventilated patients. Submit Corrections or Updates.	risk of death, 11.1% higher, RR 1.11, p = 1.00, treatment 4 of 9 (44.4%), control 4 of 10 (40.0%).
	risk of death, 25.9% lower, RR 0.74, p = 1.00, treatment 2 of 9 (22.2%), control 3 of 10 (30.0%), NNT 13, day 30.
Dominguez-Nicolas, 5/25/2021, prospective, Mexico, peer-reviewed, 2 authors, LF-ThMS, trial NCT04895267 (history), excluded in exclusion analyses: the study design does not provide a clear relative risk. Submit Corrections or Updates.	improvement in SpO₂ <5, 52.9% lower, RR 0.47, p = 0.05, treatment 8 of 17 (47.1%), control 5 of 5 (100.0%), NNT 1.9.
	no improvement in SpO₂, 93.0% lower, RR 0.07, p = 0.006, treatment 0 of 17 (0.0%), control 3 of 5 (60.0%), NNT 1.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	improvement in SpO₂ <2, 93.0% lower, RR 0.07, p = 0.006, treatment 0 of 17 (0.0%), control 3 of 5 (60.0%), NNT 1.7, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	improvement in SpO₂ <3, 95.7% lower, RR 0.04, p < 0.001, treatment 0 of 17 (0.0%), control 5 of 5 (100.0%), NNT 1.0, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm).
	improvement in SpO₂ <4, 82.4% lower, RR 0.18, p = 0.002, treatment 3 of 17 (17.6%), control 5 of 5 (100.0%), NNT 1.2.
	improvement in SpO₂ <5, 52.9% lower, RR 0.47, p = 0.05, treatment 8 of 17 (47.1%), control 5 of 5 (100.0%), NNT 1.9.
	improvement in SpO₂ <6, 35.3% lower, RR 0.65, p = 0.27, treatment 11 of 17 (64.7%), control 5 of 5 (100.0%), NNT 2.8.
	improvement in SpO₂ <7, 35.3% lower, RR 0.65, p = 0.27, treatment 11 of 17 (64.7%), control 5 of 5 (100.0%), NNT 2.8.
	improvement in SpO₂ <8, 17.6% lower, RR 0.82, p = 1.00, treatment 14 of 17 (82.4%), control 5 of 5 (100.0%), NNT 5.7.
	improvement in SpO₂ <9, 17.6% lower, RR 0.82, p = 1.00, treatment 14 of 17 (82.4%), control 5 of 5 (100.0%), NNT 5.7.
	improvement in SpO₂ <10, 11.8% lower, RR 0.88, p = 1.00, treatment 15 of 17 (88.2%), control 5 of 5 (100.0%), NNT 8.5.
	improvement in SpO₂ <11, 5.9% lower, RR 0.94, p = 1.00, treatment 16 of 17 (94.1%), control 5 of 5 (100.0%), NNT 17.
Huang, 2/1/2021, Randomized Controlled Trial, China, peer-reviewed, 8 authors, study period 18 February, 2020 - 20 April, 2020, diathermy, trial ChiCTR2000029972. Submit Corrections or Updates.	risk of oxygen therapy, 80.0% lower, RR 0.20, p = 0.49, treatment 0 of 25 (0.0%), control 2 of 25 (8.0%), NNT 12, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 28.
	risk of hospitalization, 40.0% lower, RR 0.60, p = 0.70, treatment 3 of 25 (12.0%), control 5 of 25 (20.0%), NNT 12, day 28.
	clinical scale >1, 66.7% lower, RR 0.33, p = 0.002, treatment 6 of 25 (24.0%), control 18 of 25 (72.0%), NNT 2.1, day 28.
	recovery time, 15.4% lower, relative time 0.85, p = 0.04, treatment 25, control 25.
	SIRS, 66.7% lower, RR 0.33, p = 0.03, treatment 25, control 25, inverted to make RR<1 favor treatment, day 7.
	SIRS, 66.7% lower, RR 0.33, p = 0.002, treatment 25, control 25, inverted to make RR<1 favor treatment, day 14.
	risk of no viral clearance, 80.0% lower, RR 0.20, p = 0.49, treatment 0 of 25 (0.0%), control 2 of 25 (8.0%), NNT 12, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), day 28.
	risk of no viral clearance, 50.0% lower, RR 0.50, p = 0.46, treatment 3 of 25 (12.0%), control 6 of 25 (24.0%), NNT 8.3, day 21.
	risk of no viral clearance, 57.1% higher, RR 1.57, p = 0.38, treatment 11 of 25 (44.0%), control 7 of 25 (28.0%), day 14.
	risk of no viral clearance, 27.8% higher, RR 1.28, p = 0.14, treatment 23 of 25 (92.0%), control 18 of 25 (72.0%), day 7.
Mancilla-Galindo (B), 12/22/2023, Randomized Controlled Trial, Mexico, peer-reviewed, median age 53.0, 15 authors, study period 27 August, 2020 - 23 August, 2021, heating pad, trial NCT04363541 (history) (TherMoCoV). Submit Corrections or Updates.	risk of death, 43.3% lower, RR 0.57, p = 0.28, treatment 6 of 54 (11.1%), control 10 of 51 (19.6%), NNT 12, day 28.
	risk of death, 43.3% lower, RR 0.57, p = 0.48, treatment 3 of 54 (5.6%), control 5 of 51 (9.8%), NNT 24, day 14.
	risk of mechanical ventilation, 21.3% lower, RR 0.79, p = 0.76, treatment 5 of 54 (9.3%), control 6 of 51 (11.8%), NNT 40.
	risk of progression, 17.4% lower, RR 0.83, p = 0.67, treatment 14 of 54 (25.9%), control 16 of 51 (31.4%), NNT 18.
Tian, 3/31/2022, Double Blind Randomized Controlled Trial, placebo-controlled, China, peer-reviewed, 12 authors, study period 1 March, 2020 - 5 April, 2020, diathermy. Submit Corrections or Updates.	risk of mechanical ventilation, 84.0% lower, RR 0.16, p = 0.09, treatment 1 of 27 (3.7%), control 3 of 13 (23.1%), NNT 5.2.
	risk of ICU admission, 75.9% lower, RR 0.24, p = 0.07, treatment 2 of 27 (7.4%), control 4 of 13 (30.8%), NNT 4.3.
	clinical improvement, 67.2% lower, HR 0.33, p = 0.005, treatment 27, control 13, inverted to make HR<1 favor treatment, Cox proportional hazards.
	CT improvement, 73.1% lower, HR 0.27, p = 0.005, treatment 27, control 13, inverted to make HR<1 favor treatment, Cox proportional hazards.

Supplementary Data

Footnotes

Viral infection and replication involves attachment, entry, uncoating and release, genome replication and transcription, translation and protein processing, assembly and budding, and release. Each step can be disrupted by therapeutics.

Monoclonal antibodies were previously included. Other treatments such as dexamethasone, tocilizumab, and baricitinib were recommended for late stage hospitalized patients.

Smoking was known to cause lung cancer since at least 1939, but this was not widely recognized in the US until 1964, 25 years later. Surgeon general Leroy Burney tried publicizing the danger starting in 1957, with limited success. Surgeon general Luther Terry, appointed in 1961, prompted by President Kennedy in 1962 amid pressure from health advocates, finally got recognition in 1964. The 1964 report reviewed 7,000+ studies, but these could (and should) have been reviewed and acted upon in real-time as they were published. Historians attribute the 25 year delay to an industry campaign to manufacture doubt and controversy, through tactics like funding biased and fraudulent research from "independent" organizations, attacking scientists, and political lobbying. The success of the industry campaign is only possible because officials did not analyze the research in detail. Fraudulent industry research supported prior failures, but would have been called out as fraudulent by officials that analyzed and understood the research in real-time.

When administered late in infection, HCQ may enhance viral egress by further increasing lysosomal pH beyond the effect of ORF3a's water channel activity, thereby promoting lysosomal exocytosis, inactivating degradative enzymes, and facilitating the release of SARS-CoV-2 particles into the extracellular environment135,136. Research also suggests potential cardioprotective effects at lower doses, but cardiotoxicity with excessive dosage137. Bobrowski et al. also indicate negative effects if HCQ and remdesivir are combined.

Peters et al. is subject to confounding by calendar-time (SOC evolved rapidly early in the pandemic, the linear covariate does not reflect non-linear SOC changes and hospital specific effects), hospital type (non-treatment hospitals were tertiary university centers), confounding by indication (4/7 hospitals initiated treatment on deterioration), immortal-time bias for as-treated (exposure assigned after baseline), significant differences for other experimental treatments, potential overadjustment from collider bias (steroid use and indication bias), limited baseline severity information, differences in hospice referral propensity across hospitals, unadjusted difference in time from onset to admission, difference in PCR positivity, and other factors. Mahévas et al. is subject to confounding by hospital (treatment highly dependent on the hospital, different SOC/ICU transfer practices, not included in PS), immortal time (only partly addressed in sensitivity analysis), co-treatment differences, calendar-time (SOC evolved rapidly early in the pandemic), binary coding for age (age ≥65 despite steep age-risk gradient), residual imbalance (variables dropped from PS), a composite outcome dependent on hospital triage/capacity, and other factors.

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