Remdesivir and systemic corticosteroids for the treatment of COVID-19: A Bayesian re-analysis
Bibliographic record
Abstract
BACKGROUND: The global death toll from coronavirus disease 2019 (COVID-19) has exceeded 2 million, and treatments to decrease mortality are needed urgently. OBJECTIVES: To examine the probabilities of a clinically meaningful reduction in mortality for remdesivir and systemic corticosteroids. DESIGN, SETTING AND PARTICIPANTS: This was a probabilistic re-analysis of clinical trial data for corticosteroids and remdesivir in the treatment of hospitalized patients with COVID-19 using a Bayesian random effects meta-analytic approach. Studies were identified from existing meta-analyses performed by the World Health Organization. MAIN OUTCOMES AND MEASURES: Posterior probabilities of an absolute decrease in mortality compared with control patients, by subgroups based on oxygen requirements, were calculated for corticosteroids and remdesivir. Probabilities of ≥1%, ≥2% and ≥5% absolute decrease in mortality were quantified. RESULTS: For patients needing mechanical ventilation, the probability of ≥1% absolute decrease in mortality was 4% for remdesivir and 93% for corticosteroids. For patients needing supplemental oxygen without mechanical ventilation, the probability of ≥1% absolute decrease in mortality was 81% for remdesivir and 93% for dexamethasone. Finally, for patients who did not need oxygen support, the probability of ≥1% absolute decrease in mortality was 29% for remdesivir and 4% for dexamethasone. CONCLUSIONS AND RELEVANCE: Using a Bayesian analytic approach, remdesivir had low probability of achieving a clinically meaningful reduction in mortality, except for patients needing supplemental oxygen without mechanical ventilation. Corticosteroids were more promising for patients needing oxygen support, especially mechanical ventilation. While awaiting more definitive studies, this probabilistic interpretation of the evidence will help to guide treatment decisions for clinicians, as well as guideline and policy makers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.110 | 0.174 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.036 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".