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Cannabidiol for COVID-19: real-time meta analysis of 3 studies
Covid Analysis, May 22, 2022, DRAFT
https://c19early.com/cbdmeta.html
0 0.5 1 1.5+ All studies -53% 3 1,153 Improvement, Studies, Patients Relative Risk Mortality -181% 1 0 Hospitalization -557% 1 91 Cases 32% 2 1,062 RCTs -557% 1 91 Symptomatic -207% 2 91 Prophylaxis -12% 2 1,062 Late -557% 1 91 Cannabidiol for COVID-19 c19early.com/cbd May 2022 Favorscannabidiol Favorscontrol
Meta analysis using the most serious outcome reported shows 53% [-68‑616%] higher risk, without reaching statistical significance.
While non-symptomatic case results show 32% [-6‑57%] improvement, symptomatic results show 207% [20‑685%] higher risk.
0 0.5 1 1.5+ All studies -53% 3 1,153 Improvement, Studies, Patients Relative Risk Mortality -181% 1 0 Hospitalization -557% 1 91 Cases 32% 2 1,062 RCTs -557% 1 91 Symptomatic -207% 2 91 Prophylaxis -12% 2 1,062 Late -557% 1 91 Cannabidiol for COVID-19 c19early.com/cbd May 2022 Favorscannabidiol Favorscontrol
Currently there is limited data, with only 48 control events for the most serious outcome in trials to date.
All data to reproduce this paper and sources are in the appendix.
Highlights
Cannabidiol reduces risk for COVID-19 with low confidence for cases, however increased risk is seen with high confidence for mortality and very low confidence for hospitalization and recovery.
We show traditional outcome specific analyses and combined evidence from all studies, incorporating treatment delay, a primary confounding factor in COVID-19 studies.
Real-time updates and corrections, transparent analysis with all results in the same format, consistent protocol for 42 treatments.
A
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Crippa (DB RCT) -557% 6.57 [0.35-124] hosp. 3/49 0/42 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.21 Late treatment -557% 6.57 [0.35-124] 3/49 0/42 -557% improvement Nguyen 50% 0.50 [0.31-0.82] cases 26/531 48/531 Improvement, RR [CI] Treatment Control Huang -181% 2.81 [1.04-7.58] death n/a n/a Tau​2 = 1.32, I​2 = 89.4%, p = 0.9 Prophylaxis -12% 1.12 [0.21-6.03] 26/531 48/531 -12% improvement All studies -53% 1.53 [0.32-7.16] 29/580 48/573 -53% improvement 3 cannabidiol COVID-19 studies c19early.com/cbd May 2022 Tau​2 = 1.36, I​2 = 82.9%, p = 0.6 Effect extraction pre-specified(most serious outcome, see appendix) Favors cannabidiol Favors control
Figure 1. A. Random effects meta-analysis. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix. B. Scatter plot showing the distribution of effects reported in studies. C. History of all reported effects (chronological within treatment stages).
Introduction
We analyze all significant studies concerning the use of cannabidiol for COVID-19. 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, for studies within each treatment stage, for individual outcomes, for peer-reviewed studies, for Randomized Controlled Trials (RCTs), and after exclusions.
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.
Figure 2. Treatment stages.
Preclinical Research
An In Vitro study supports the efficacy of cannabidiol [van Breemen].
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.
Results
Figure 3 shows a visual overview of the results, with details in Table 1 and Table 2. Figure 4, 5, 6, 7, 8, and 9 show forest plots for a random effects meta-analysis of all studies with pooled effects, mortality results, hospitalization, recovery, cases, and non-symptomatic vs. symptomatic results.
0 0.5 1 1.5+ ALL STUDIES MORTALITY HOSPITALIZATION CASES RCTS All Prophylaxis Late Cannabidiol for COVID-19 C19EARLY.COM/CBD MAY 2022
Figure 3. Overview of results.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Late treatment 0 1 0.0% -557% improvement
RR 6.57 [0.35‑123.69]
p = 0.21
Prophylaxis 1 2 50.0% -12% improvement
RR 1.12 [0.21‑6.03]
p = 0.9
All studies 1 3 33.3% -53% improvement
RR 1.53 [0.32‑7.16]
p = 0.6
Table 1. Results by treatment stage.
Studies Late treatment Prophylaxis PatientsAuthors
All studies 3-557% [-12269‑65%]-12% [-503‑79%] 1,153 69
Randomized Controlled TrialsRCTs 1-557% [-12269‑65%] 91 32
Table 2. Results by treatment stage for all studies and with different exclusions.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Crippa (DB RCT) -557% 6.57 [0.35-124] hosp. 3/49 0/42 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.21 Late treatment -557% 6.57 [0.35-124] 3/49 0/42 -557% improvement Nguyen 50% 0.50 [0.31-0.82] cases 26/531 48/531 Improvement, RR [CI] Treatment Control Huang -181% 2.81 [1.04-7.58] death n/a n/a Tau​2 = 1.32, I​2 = 89.4%, p = 0.9 Prophylaxis -12% 1.12 [0.21-6.03] 26/531 48/531 -12% improvement All studies -53% 1.53 [0.32-7.16] 29/580 48/573 -53% improvement 3 cannabidiol COVID-19 studies c19early.com/cbd May 2022 Tau​2 = 1.36, I​2 = 82.9%, p = 0.6 Effect extraction pre-specified(most serious outcome, see appendix) Favors cannabidiol Favors control
Figure 4. Random effects meta-analysis for all studies with pooled effects. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Huang -181% 2.81 [1.04-7.58] n/a n/a Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.041 Prophylaxis -181% 2.81 [1.04-7.58] -181% improvement All studies -181% 2.81 [1.04-7.58] -181% improvement 1 cannabidiol COVID-19 mortality result c19early.com/cbd May 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.041 Favors cannabidiol Favors control
Figure 5. Random effects meta-analysis for mortality results.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Crippa (DB RCT) -557% 6.57 [0.35-124] hosp. 3/49 0/42 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.21 Late treatment -557% 6.57 [0.35-124] 3/49 0/42 -557% improvement All studies -557% 6.57 [0.35-124] 3/49 0/42 -557% improvement 1 cannabidiol COVID-19 hospitalization result c19early.com/cbd May 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.21 Favors cannabidiol Favors control
Figure 6. Random effects meta-analysis for hospitalization.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Crippa (DB RCT) -33% 1.33 [0.86-2.08] recov. time 49 (n) 42 (n) Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.2 Late treatment -33% 1.33 [0.86-2.08] 0/49 0/42 -33% improvement All studies -33% 1.33 [0.86-2.08] 0/49 0/42 -33% improvement 1 cannabidiol COVID-19 recovery result c19early.com/cbd May 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.2 Favors cannabidiol Favors control
Figure 7. Random effects meta-analysis for recovery.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Nguyen 50% 0.50 [0.31-0.82] cases 26/531 48/531 Improvement, RR [CI] Treatment Control Huang 19% 0.81 [0.73-0.90] cases n/a n/a Tau​2 = 0.08, I​2 = 74.1%, p = 0.089 Prophylaxis 32% 0.68 [0.43-1.06] 26/531 48/531 32% improvement All studies 32% 0.68 [0.43-1.06] 26/531 48/531 32% improvement 2 cannabidiol COVID-19 case results c19early.com/cbd May 2022 Tau​2 = 0.08, I​2 = 74.1%, p = 0.089 Favors cannabidiol Favors control
Figure 8. Random effects meta-analysis for cases.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Crippa (DB RCT) -557% 6.57 [0.35-124] hosp. 3/49 0/42 Improvement, RR [CI] Treatment Control Huang -181% 2.81 [1.04-7.58] death n/a n/a Tau​2 = 0.00, I​2 = 0.0%, p = 0.019 Symptomatic -207% 3.07 [1.20-7.85] 3/49 0/42 -207% improvement Nguyen 50% 0.50 [0.31-0.82] cases 26/531 48/531 Improvement, RR [CI] Treatment Control Huang 19% 0.81 [0.73-0.90] cases n/a n/a Tau​2 = 0.08, I​2 = 74.1%, p = 0.089 Cases 32% 0.68 [0.43-1.06] 26/531 48/531 32% improvement All studies 5% 0.95 [0.52-1.72] 29/580 48/573 5% improvement 4 cannabidiol COVID-19 symptomatic outcomes vs. case outcomes c19early.com/cbd May 2022 Tau​2 = 0.21, I​2 = 75.1%, p = 0.87 Effect extraction pre-specified(most serious outcome, see appendix) Favors cannabidiol Favors control
Figure 9. Random effects meta-analysis for non-symptomatic vs. symptomatic results. Effect extraction is pre-specified, using the most serious outcome reported, see the appendix for details.
Randomized Controlled Trials (RCTs)
Figure 10 shows a forest plot for random effects meta-analysis of all Randomized Controlled Trials. Table 3 summarizes the results. Currently there is only one RCT.
0 0.25 0.5 0.75 1 1.25 1.5 1.75 2+ Crippa (DB RCT) -557% 6.57 [0.35-124] hosp. 3/49 0/42 Improvement, RR [CI] Treatment Control Tau​2 = 0.00, I​2 = 0.0%, p = 0.21 Late treatment -557% 6.57 [0.35-124] 3/49 0/42 -557% improvement All studies -557% 6.57 [0.35-124] 3/49 0/42 -557% improvement 1 cannabidiol COVID-19 Randomized Controlled Trials c19early.com/cbd May 2022 Tau​2 = 0.00, I​2 = 0.0%, p = 0.21 Effect extraction pre-specified(most serious outcome, see appendix) Favors cannabidiol Favors control
Figure 10. Random effects meta-analysis for all Randomized Controlled Trials. This plot shows pooled effects, discussion can be found in the heterogeneity section, and results for specific outcomes can be found in the individual outcome analyses. Effect extraction is pre-specified, using the most serious outcome reported. For details of effect extraction see the appendix.
Treatment timeNumber of studies reporting positive effects Total number of studiesPercentage of studies reporting positive effects Random effects meta-analysis results
Randomized Controlled Trials 0 1 0.0% -557% improvement
RR 6.57 [0.35‑123.69]
p = 0.21
Table 3. Randomized Controlled Trial results.
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 hours [McLean, Treanor]. Figure 11 shows a mixed-effects meta-regression for efficacy as a function of treatment delay in COVID-19 studies from 42 treatments, showing that efficacy declines rapidly with treatment delay. Early treatment is critical for COVID-19.
Figure 11. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 42 treatments. Early treatment is critical.
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 (as in [López-Medina]).
Effect measured.
Efficacy may differ significantly depending on the effect measured, for example a treatment may be very effective at reducing mortality, but less effective at minimizing cases or hospitalization. Or a treatment may have no effect on viral clearance while still being effective at reducing mortality.
Variants.
There are many different variants of SARS-CoV-2 and efficacy may depend critically on the distribution of variants encountered by the patients in a study. For example, the Gamma variant shows significantly different characteristics [Faria, Karita, Nonaka, Zavascki]. Different mechanisms of action may be more or less effective depending on variants, for example the viral entry process for the omicron variant has moved towards TMPRSS2-independent fusion, suggesting that TMPRSS2 inhibitors may be less effective [Peacock, Willett].
Regimen.
Effectiveness may depend strongly on the dosage and treatment regimen.
Treatments.
The use of other treatments may significantly affect outcomes, including anything from supplements, other medications, or other kinds of treatment such as prone positioning.
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 the treatment may be very effective when used earlier.
In general, by combining heterogeneous studies, as all meta analyses do, we run the risk of obscuring an effect by including studies where the treatment is less effective, not effective, or harmful.
When including studies where a treatment is less effective we expect the estimated effect size to be lower than that for the optimal case. We do not a priori expect that pooling all studies will create a positive result for an effective treatment. 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.
Discussion
Publication bias.
Publishing is often biased towards positive results. Trials with patented drugs may have a financial conflict of interest that results in positive studies being more likely to be published, or bias towards more positive results. For example with molnupiravir, trials with negative results remain unpublished to date (CTRI/2021/05/033864 and CTRI/2021/08/0354242). For cannabidiol, there is currently not enough data to evaluate publication bias with high confidence.
Funnel plot analysis.
Funnel plots have traditionally been used for analyzing publication bias. This is invalid for COVID-19 acute treatment trials — the underlying assumptions are invalid, which we can demonstrate with a simple example. Consider a set of hypothetical perfect trials with no bias. Figure 12 plot A shows a funnel plot for a simulation of 80 perfect trials, with random group sizes, and each patient's outcome randomly sampled (10% control event probability, and a 30% effect size for treatment). Analysis shows no asymmetry (p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment trials — treatment delay. Consider that efficacy varies from 90% for treatment within 24 hours, reducing to 10% when treatment is delayed 3 days. In plot B, each trial's treatment delay is randomly selected. Analysis now shows highly significant asymmetry, p < 0.0001, with six variants of Egger's test all showing p < 0.05 [Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley]. Note that these tests fail even though treatment delay is uniformly distributed. In reality treatment delay is more complex — each trial has a different distribution of delays across patients, and the distribution across trials may be biased (e.g., late treatment trials may be more common). Similarly, many other variations in trials may produce asymmetry, including dose, administration, duration of treatment, differences in SOC, comorbidities, age, variants, and bias in design, implementation, analysis, and reporting.
Figure 12. Example funnel plot analysis for simulated perfect trials.
Early/late vs. mild/moderate/severe.
Some analyses classify treatment based on early/late administration (as we do here), while others distinguish between mild/moderate/severe cases. We note that 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.
Conclusion
Meta analysis using the most serious outcome reported shows 53% [-68‑616%] higher risk, without reaching statistical significance. While non-symptomatic case results show 32% [-6‑57%] improvement, symptomatic results show 207% [20‑685%] higher risk.
Currently there is limited data, with only 48 control events for the most serious outcome in trials to date.
Study Notes
0 0.5 1 1.5 2+ Hospitalization -557% Improvement Relative Risk Recovery time -33% c19early.com/cbd Crippa et al. Cannabidiol for COVID-19 RCT LATE TREATMENT Favors cannabidiol Favors control
[Crippa] RCT 105 patients recruited in an ER in Brazil, 49 treated with CBD, showing no significant differences with treatment. 300mg CBD for 14 days.

For discussion see [liebertpub.com].
0 0.5 1 1.5 2+ Mortality -181% Improvement Relative Risk Case 19% c19early.com/cbd Huang et al. Cannabidiol for COVID-19 Prophylaxis Favors cannabidiol Favors control
[Huang] UK Biobank retrospective with 13,099 cannabis users, showing a lower risk of COVID-19 infection, however regular users had a significantly higher risk of mortality.
0 0.5 1 1.5 2+ Case 50% Improvement Relative Risk Case (b) 33% c19early.com/cbd Nguyen et al. Cannabidiol for COVID-19 Prophylaxis Favors cannabidiol Favors control
[Nguyen] Retrospective 1,212 patients in the USA with a history of seizure-related conditions, showing patients treated with CBD100 had significantly lower incidence of COVID-19 cases compared to a matched control group.

In Vitro study showing CBD inhibits SARS-CoV-2 with Vero E6 and Calu-3 cells. Mouse study showing CBD significantly inhibited viral replication in the lung and nasal turbinate.

Authors note that CBD does not inhibit ACE2 expression or the main viral proteases, inhibition occurs after viral entry. Authors stress several limitations for use at this time, including purity, quality, and the formulation of products, and potential lung damage based on administration method.

Authors recommend clinical trials, but do not mention the existing RCT by Crippa et al.
We performed ongoing searches of PubMed, medRxiv, ClinicalTrials.gov, The Cochrane Library, Google Scholar, Collabovid, Research Square, ScienceDirect, Oxford University Press, the reference lists of other studies and meta-analyses, and submissions to the site c19early.com. Search terms were cannabidiol, filtered for papers containing the terms COVID-19 or SARS-CoV-2. Automated searches are performed every few hours with notification of new matches. All studies regarding the use of cannabidiol for COVID-19 that report a comparison with a control group are included in the main analysis. 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 are used. Mortality alone is preferred over combined outcomes. Outcomes with zero events in both arms were not used (the next most serious outcome is used — no studies were excluded). 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 outcome is considered more important than PCR testing 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 no room for an effective treatment to do better). 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 computed the relative risk when possible, or converted to a relative risk according to [Zhang]. Reported confidence intervals and p-values were used when available, using adjusted values when provided. If multiple types of adjustments are reported including propensity score matching (PSM), the PSM results are used. Adjusted primary outcome 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 1 [Sweeting]. 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 (3.9.12) with scipy (1.8.0), pythonmeta (1.26), numpy (1.22.2), statsmodels (0.14.0), and plotly (5.6.0).
Forest plots are computed using PythonMeta [Deng] with the DerSimonian and Laird random effects model (the fixed effect assumption is not plausible in this case) and inverse variance weighting. Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor (3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
We received no funding, this research is done in our spare time. We have no affiliations with any pharmaceutical companies or political parties.
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 effective [McLean, Treanor].
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.com/cbdmeta.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.
[Crippa], 10/7/2021, Double Blind Randomized Controlled Trial, placebo-controlled, Brazil, South America, peer-reviewed, 32 authors, study period 7 July, 2020 - 16 October, 2020. risk of hospitalization, 557.1% higher, RR 6.57, p = 0.25, treatment 3 of 49 (6.1%), control 0 of 42 (0.0%), continuity correction due to zero event (with reciprocal of the contrasting arm).
recovery time, 33.3% higher, relative time 1.33, p = 0.20, treatment 49, control 42.
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.
[Huang], 3/8/2022, retrospective, United Kingdom, Europe, peer-reviewed, 3 authors. risk of death, 181.0% higher, HR 2.81, p = 0.04, regular users, Cox proportional hazards.
risk of case, 19.0% lower, OR 0.81, p < 0.001, adjusted per study, multivariable, RR approximated with OR.
[Nguyen], 1/20/2022, retrospective, USA, North America, peer-reviewed, 34 authors. risk of case, 49.6% lower, RR 0.50, p = 0.006, treatment 26 of 531 (4.9%), control 48 of 531 (9.0%), NNT 24, odds ratio converted to relative risk, active CBD100 users.
risk of case, 32.9% lower, RR 0.67, p = 0.009, treatment 75 of 1,212 (6.2%), control 108 of 1,212 (8.9%), NNT 37, odds ratio converted to relative risk, all CBD100 users.
Supplementary Data
References
Please send us corrections, updates, or comments. Vaccines and treatments are both valuable and complementary. All practical, effective, and safe means should be used. No treatment, vaccine, or intervention is 100% available and effective for all current and future variants. Denying the efficacy of any method increases mortality, morbidity, collateral damage, and the risk of endemic status. 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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