Tocilizumab in COVID-19 – A Bayesian reanalysis of RECOVERY
Bibliographic record
Abstract
Abstract Background Randomised Evaluation of COVID-19 Therapy (RECOVERY) demonstrated that tocilizumab reduces mortality in hospitalized COVID-19 patients. However, substantial uncertainty remains whether tocilizumab’s effect is similar across clinically relevant subgroups. Whether this uncertainty can be resolved with Bayesian methods is unknown. Design, Setting, Participants, and Interventions RECOVERY was a controlled, open-label, platform UK trial that randomized (1:1) 4116 adults with oxygen saturation <92% on room air or receiving oxygen therapy with C-reactive protein ≥75 mg/L to either usual care or tocilizumab plus usual care. Main outcome measures Mortality and hospital discharge within 28 days. Methods Using Bayesian methods, we combined RECOVERY with evidence-based priors in-corporating previous COVID-19 tocilizumab RCTs. The probability of tocilizumab’s benefit for respiratory support and corticosteroid subgroups and sensitivity analyses were performed with different prior distributions and baseline risks. Results For all-cause mortality, the posterior probabilities of decreased deaths with tocilizumab were >99% and 19% in patients using and not using corticosteroids, respectively. In patients on simple oxygen only, non-invasive ventilation and invasive mechanical ventilation, the probabilities of decreased mortality were 96%, >99% and 77%, respectively. The probabilities for a clinically significant mortality reduction, as assessed by an absolute risk difference > 3% (number needed to treat ≤ 33), were 77%, 96%, 56%, respectively. Sensitivity analyses highlighted the uncertainty and lack of conclusive evidence for tocilizumab’s effect in patients on invasive mechanical ventilation and those without concurrent corticosteroids. Posterior probabilities of benefit for hospital discharge outcome were high and consistent across most subgroups. Conclusions In this Bayesian reanalysis, COVID-19 hospitalized patients exposed to corticosteroids or on non-invasive ventilation have a high probability of a clinically meaningful mortality benefit from tocilizumab. Tocilizumab also likely improves discharge from hospital in most subgroups. Future research should further address if patients on invasive mechanical ventilation can also benefit from tocilizumab.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.154 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".