Tocilizumab, sarilumab and anakinra in critically ill patients with COVID-19: a randomised, controlled, open-label, adaptive platform trial
Bibliographic record
Abstract
INTRODUCTION: Tocilizumab improves outcomes in critically ill patients with COVID-19. Whether other immune-modulator strategies are equally effective or better is unknown. METHODS: We investigated treatment with tocilizumab, sarilumab, anakinra and no immune modulator in these patients. In this ongoing, adaptive platform trial in 133 sites in 9 countries, we randomly assigned patients with allocation ratios dependent on the number of interventions available at each site. The primary outcome was an ordinal scale combining in-hospital mortality (assigned -1) and days free of organ support to day 21 in survivors. The trial used a Bayesian statistical model with predefined triggers for superiority, inferiority, efficacy, equivalence or futility. RESULTS: Of 2274 critically ill participants enrolled between 25 March 2020 and 10 April 2021, 972 were assigned to tocilizumab, 485 to sarilumab, 378 to anakinra and 418 to control. Median organ support-free days were 7 (IQR -1, 16), 9 (IQR -1, 17), 0 (IQR -1, 15) and 0 (IQR -1, 15) for tocilizumab, sarilumab, anakinra and control, respectively. Median adjusted ORs were 1.46 (95% credible intervals (CrI) 1.13, 1.87), 1.50 (95% CrI 1.13, 2.00) and 0.99 (95% CrI 0.74, 1.35) for tocilizumab, sarilumab and anakinra relative to control, yielding 99.8%, 99.8% and 46.6% posterior probabilities of superiority, respectively, compared with control. All treatments appeared safe. CONCLUSIONS: In critically ill patients with COVID-19, tocilizumab and sarilumab have equivalent effectiveness at reducing duration of organ support and death. Anakinra is not effective in this population. TRIAL REGISTRATION NUMBER: NCT02735707.
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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 | 0.161 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".