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Record W3175835730 · doi:10.1101/2021.06.15.21258966

Tocilizumab in COVID-19 – A Bayesian reanalysis of RECOVERY

2021· preprint· en· W3175835730 on OpenAlexaff
Arthur M. Albuquerque, Lucas Tramujas, Lorenzo R. Sewanan, James M. Brophy

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsTocilizumabMedicineMechanical ventilationRandomized controlled trialInternal medicineOxygen therapy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.154
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.154
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.072
GPT teacher head0.434
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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