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

@CovidAnalysis, July 2024, Version 1V1
 
0 0.5 1 1.5+ All studies 56% 4 217 Improvement, Studies, Patients Relative Risk Mortality 43% 1 105 Ventilation 53% 2 145 ICU admission 76% 1 40 Hospitalization 40% 1 50 Recovery 67% 2 90 Viral clearance 80% 1 50 RCTs 56% 3 195 Late 56% 4 217 Thermotherapy for COVID-19 c19early.org July 2024 after exclusions Favorsthermotherapy Favorscontrol
Abstract
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.
Statistically significant lower risk is seen for recovery. 3 studies from 3 independent teams in 2 countries show significant improvements.
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.
0 0.5 1 1.5+ All studies 56% 4 217 Improvement, Studies, Patients Relative Risk Mortality 43% 1 105 Ventilation 53% 2 145 ICU admission 76% 1 40 Hospitalization 40% 1 50 Recovery 67% 2 90 Viral clearance 80% 1 50 RCTs 56% 3 195 Late 56% 4 217 Thermotherapy for COVID-19 c19early.org July 2024 after exclusions Favorsthermotherapy Favorscontrol
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 or intervention is 100% effective. All practical, effective, and safe means should be used based on risk/benefit analysis. Multiple treatments are typically used in combination, and other treatments may be more effective. There has been no early treatment studies to date. Thermotherapy methods may have additional mechanisms of action beyond increased internal body temperatures. Studies of ventilated patients are excluded1.
All data to reproduce this paper and sources are in the appendix.
Evolution of COVID-19 clinical evidence Thermotherapy p=0.026 Acetaminophen p=0.00000029 2020 2021 2022 2023 2024 Effective Harmful c19early.org July 2024 meta analysis results (pooled effects) 100% 50% 0% -50%
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.
45th treatment shown effective with ≥3 clinical studies in December 2023, now with p = 0.026 from 4 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 79 treatments, outcome specific analyses and combined evidence from all studies.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Huang (RCT) 80% 0.20 [0.01-3.97] oxygen 0/25 2/25 diathermy Improvement, RR [CI] Treatment Control Dominguez-Nicolas 53% 0.47 [0.22-1.01] no improv. 8/17 5/5 LF-ThMS Tian (DB RCT) 84% 0.16 [0.02-1.40] ventilation 1/27 3/13 diathermy Excluded Bonfanti (RCT) -11% 1.11 [0.39-3.19] death 4/9 4/10 Ventilated patients TherMoCoV Mancilla-G.. (RCT) 43% 0.57 [0.22-1.45] death 6/54 10/51 heating pad Tau​2 = 0.00, I​2 = 0.0%, p = 0.026 Late treatment 56% 0.44 [0.22-0.91] 15/123 20/94 56% lower risk All studies 56% 0.44 [0.22-0.91] 15/123 20/94 56% lower risk 4 thermotherapy COVID-19 studies c19early.org July 2024 Tau​2 = 0.00, I​2 = 0.0%, p = 0.026 Effect extraction pre-specified(most serious outcome, see appendix) Favors thermotherapy Favors control
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Huang (RCT) 80% oxygen therapy diathermy Improvement Relative Risk [CI] Dominguez-Nico.. 53% improvement LF-ThMS Tian (DB RCT) 84% ventilation diathermy Excluded Bonfanti (RCT) -11% death Ventilated patients TherMoCoV Mancilla-.. (RCT) 43% death heating pad Tau​2 = 0.00, I​2 = 0.0%, p = 0.026 Late treatment 56% 56% lower risk All studies 56% 56% lower risk 4 thermotherapy C19 studies c19early.org July 2024 Tau​2 = 0.00, I​2 = 0.0%, p = 0.026 Effect extraction pre-specifiedRotate device for details Favors thermotherapy Favors control
B
-100% -50% 0% 50% 100% Timeline of COVID-19 thermotherapy studies (pooled effects) 2020 2021 2022 2023 Favorsthermotherapy Favorscontrol c19early.org July 2024 December 2023: efficacy (pooled outcomes) December 2023: efficacy (RCT pooled)
Figure 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
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 injury2-9 and cognitive deficits4,9, cardiovascular complications10, organ failure, and death. Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+ host and viral proteins and other factorsA,11-15, providing many therapeutic targets for which many existing compounds have known activity. Scientists have predicted that over 7,000 compounds may reduce COVID-19 risk16, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications.
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 system17 and to minimize SARS-CoV-2 replication18.
Studies have shown efficacy with thermotherapy for pneumonia19, the common cold20, SARS-CoV-121, and influenza22.
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.
Figure 2 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.
Figure 2. Treatment stages.
Preclinical Research
An In Vitro study supports the efficacy of thermotherapy18.
An In Vivo animal study supports the efficacy of thermotherapy23.
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.
Beneficial Effects of Fever
Fever is an important component of the acute response to coronavirus infection24. The evolutionary conservation of fever for over 600 million years supports a survival benefit25. 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 systems25, 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.
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 Figure 3. 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.
Figure 3. 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 Figure 4.
Figure 4. 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.
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. Figure 5, 6, 7, 8, 9, 10, 11, and 12 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 percentage improvement with treatment and the 95% confidence interval. * p<0.05  **** p<0.0001.
Improvement Studies Patients Authors
All studies56% [9‑78%]
*
4 217 37
After exclusions56% [1‑81%]
*
3 195 35
Randomized Controlled TrialsRCTs56% [1‑81%]
*
3 195 35
VentilationVent.53% [-102‑89%]2 145 27
Recovery67% [43‑81%]
****
2 90 20
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Figure 5. 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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Figure 6. Random effects meta-analysis for mortality results.
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Figure 7. Random effects meta-analysis for ventilation.
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Figure 8. Random effects meta-analysis for ICU admission.
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Figure 9. Random effects meta-analysis for hospitalization.
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Figure 10. Random effects meta-analysis for progression.
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Figure 11. Random effects meta-analysis for recovery.
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Figure 12. Random effects meta-analysis for viral clearance.
Randomized Controlled Trials (RCTs)
Figure 13 shows a comparison of results for RCTs and non-RCT studies. Random effects meta analysis of RCTs shows 56% improvement, compared to 53% for other studies. Figure 14 and 15 show forest plots for random effects meta-analysis of all Randomized Controlled Trials and RCT mortality results. RCT results are included in Table 1.
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Figure 13. Results for RCTs and non-RCT studies.
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Figure 14. 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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Figure 15. Random effects meta-analysis for RCT mortality results.
RCTs help to make study groups more similar and can provide a higher level of evidence, however they are subject to many biases28, and analysis of double-blind RCTs has identified extreme levels of bias29. 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.
RCTs are expensive and many RCTs are funded by pharmaceutical companies or interests closely aligned with pharmaceutical companies. 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. For COVID-19, some major philanthropic organizations are largely funded by investments with extreme conflicts of interest for and against specific COVID-19 interventions.
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 79 treatments we have analyzed, 63% 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.
Evidence shows that non-RCT 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. summarized reviews comparing RCTs to observational studies and found little evidence for significant differences in effect estimates. 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 Internet 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 see34,35.
Currently, 47 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, 30 have been confirmed in RCTs, with a mean delay of 7.0 months. When considering only low cost treatments, 25 have been confirmed with a delay of 8.4 months. For the 17 unconfirmed treatments, 3 have zero RCTs to date. The point estimates for the remaining 14 are all consistent with the overall results (benefit or harm), with 11 showing >20%. The only treatments showing >10% efficacy for all studies, but <10% for RCTs are sotrovimab and aspirin.
We need to evaluate each trial on its own merits. 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.
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. Figure 16 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.
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Figure 16. 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.
Heterogeneity
Heterogeneity in COVID-19 studies arises from many factors including:
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 hours36,37. Baloxavir 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 for influenza show that early treatment is more effective.
Treatment delayResult
Post-exposure prophylaxis86% fewer cases38
<24 hours-33 hours symptoms39
24-48 hours-13 hours symptoms39
Inpatients-2.5 hours to improvement40
Figure 17 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 79 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
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Figure 17. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 79 treatments.
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.
Efficacy may depend critically on the distribution of SARS-CoV-2 variants encountered by patients. Risk varies significantly across variants42, for example the Gamma variant shows significantly different characteristics43-46. 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 variants47,48.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use of other treatments may significantly affect outcomes, including supplements, other medications, or other interventions such as prone positioning. Treatments may be synergistic49-59, therefore efficacy may depend strongly on combined treatments.
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.
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.
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
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.
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.
Another way to view pooled analysis is that we are using more of the available information. Logically we should, and do, use additional information. For example dose-response and treatment delay-response relationships provide significant additional evidence of efficacy that is considered when reviewing the evidence for a treatment.
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 collection of evidence.
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 79 treatments we cover confirms the validity of pooled outcome analysis for COVID-19. Figure 18 shows that lower hospitalization is very strongly associated with lower mortality (p < 0.000000000001). Similarly, Figure 19 shows that improved recovery is very strongly associated with lower mortality (p < 0.000000000001). 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. Figure 20 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 = 0.0000011 to p = 0.0000000036.
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Figure 18. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 19. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 18. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 47 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 91% of these have been confirmed with one or more specific outcomes, with a mean delay of 5.2 months. When restricting to RCTs only, 54% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 6.4 months. Figure 21 shows when treatments were found effective during the pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 21. 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.
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 difference in treatment delay is more likely to hide efficacy.
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.
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.
Studies have also shown efficacy with thermotherapy for pneumonia19, the common cold20, SARS-CoV-121, and influenza22.
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 results63-66. For thermotherapy, there is currently not enough data to evaluate publication bias with high confidence.
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 alone49-59. 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.
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.
Many reviews cover thermotherapy for COVID-19, presenting additional background on mechanisms and related results, including25,27,67-70.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host and viral proteins and other factors11-15, providing many therapeutic targets. Over 7,000 compounds have been predicted to reduce COVID-19 risk16, either by directly minimizing infection or replication, by supporting immune system function, or by minimizing secondary complications. Thermotherapy is known to modulate the immune system17 and to minimize SARS-CoV-2 replication18. Figure 22 shows an overview of the results for thermotherapy in the context of multiple COVID-19 treatments, and Figure 23 shows a plot of efficacy vs. cost for COVID-19 treatments.
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Figure 22. Scatter plot showing results within the context of multiple COVID-19 treatments. Diamonds shows the results of random effects meta-analysis. 0.6% of 7,000+ proposed treatments show efficacy71.
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Figure 23. Efficacy vs. cost for COVID-19 treatments.
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 system17 and to minimize SARS-CoV-2 replication18.
Studies to date show that thermotherapy is an effective treatment for COVID-19. Statistically significant lower risk is seen for recovery. 3 studies from 3 independent teams in 2 countries show significant improvements. 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.
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.
Improvement in SpO2 <5 53% Improvement Relative Risk Improvement in SpO2 93% Improvement in SpO2 <2 93% Improvement in SpO2 <3 96% Improvement in SpO2 <4 82% Improvement in SpO2 <5 (b) 53% Improvement in SpO2 <6 35% Improvement in SpO2 <7 35% Improvement in SpO2 <8 18% Improvement in SpO2 <9 18% Improvement in SpO2 <10 12% Improvement in SpO2 <11 6% Thermotherapy  Dominguez-Nicolas et al.  LATE TREATMENT Is late treatment with thermotherapy beneficial for COVID-19? Prospective study of 22 patients in Mexico Greater improvement with thermotherapy (not stat. sig., p=0.054) c19early.org Dominguez-Nicolas et al., Medicine, May 2021 Favorsthermotherapy Favorscontrol 0 0.5 1 1.5 2+
Dominguez-Nicolas: 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.
Oxygen therapy 80% Improvement Relative Risk Hospitalization 40% Clinical scale >1 67% Recovery time 15% SIRS, day 7 67% SIRS, day 14 67% Viral clearance, day 28 80% Viral clearance, day 21 50% Viral clearance, day 14 -57% Viral clearance, day 7 -28% Thermotherapy  Huang et al.  LATE TREATMENT  RCT Is late treatment with thermotherapy beneficial for COVID-19? RCT 50 patients in China (February - April 2020) Improved recovery with thermotherapy (p=0.0016) c19early.org Huang et al., Frontiers in Medicine, Feb 2021 Favorsthermotherapy Favorscontrol 0 0.5 1 1.5 2+
Huang: 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%.
Mortality, day 28 43% Improvement Relative Risk Mortality, day 14 43% Ventilation 21% Progression 17% Thermotherapy  TherMoCoV  LATE TREATMENT  RCT Is late treatment with thermotherapy beneficial for COVID-19? RCT 105 patients in Mexico (August 2020 - August 2021) Lower mortality with thermotherapy (not stat. sig., p=0.28) c19early.org Mancilla-Galindo et al., Frontiers in .., Dec 2023 Favorsthermotherapy Favorscontrol 0 0.5 1 1.5 2+
Mancilla-Galindo (B): 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.
Ventilation 84% Improvement Relative Risk ICU admission 76% Clinical improvement 67% CT improvement 73% Diathermy  Tian et al.  LATE TREATMENT  DB RCT Is late treatment with diathermy beneficial for COVID-19? Double-blind RCT 40 patients in China (March - April 2020) Improved recovery with diathermy (p=0.005) c19early.org Tian et al., European J. Physical and .., Mar 2022 Favorsdiathermy Favorscontrol 0 0.5 1 1.5 2+
Tian: 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.
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. 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. This is a living analysis and is updated regularly.
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 both reported, the effect for mortality is used, this may be different to the effect that a study focused on. If symptomatic results are reported at multiple times, we used 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 test status. 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. 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. When results provide an odds ratio, we compute the relative risk when possible, or convert to a relative risk according to75. Reported confidence intervals and p-values were used when available, using adjusted values 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 178. 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 (3.12.4) with scipy (1.14.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.2), and plotly (5.22.0).
Forest plots are computed using PythonMeta79 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. 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 0.8.0 is used to parse PDF documents.
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 effective36,37.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
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.
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. 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. 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. 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). 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. 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.
Please send us corrections, updates, or comments. c19early involves the extraction of 100,000+ datapoints from thousands of papers. Community updates help ensure high accuracy. Treatments and other interventions are complementary. All practical, effective, and safe means should be used based on risk/benefit analysis. No treatment or intervention is 100% available and effective for all current and future variants. We do not provide medical advice. Before taking any medication, consult a qualified physician who can provide personalized advice and details of risks and benefits based on your medical history and situation. FLCCC and WCH provide treatment protocols.
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