Rapid rEcognition of COrticosteRoiD resistant or sensitive Sepsis (RECORDS): study protocol for a multicentre, placebo-controlled, biomarker-guided, adaptive Bayesian design basket trial
Bibliographic record
Abstract
INTRODUCTION: Corticosteroids affect variably survival in sepsis trials, suggesting heterogeneity in patients' response to corticosteroids. The RECORDS (Rapid rEcognition of COrticosteRoiD resistant or sensitive Sepsis) trial aimed at defining endotypes associated with adults with sepsis responsiveness to corticosteroids. METHODS AND ANALYSIS: RECORDS, a multicentre, placebo-controlled, biomarker-guided, adaptive Bayesian design basket trial, will randomly assign to a biomarker stratum 1800 adults with community-acquired pneumonia, vasopressor-dependent sepsis, septic shock or acute respiratory distress syndrome. In each stratum, patients will be randomly assigned to receive a 7-day course of hydrocortisone and fludrocortisone or their placebos. Patients with COVID-19 will be treated with a 10-day standard course of dexamethasone and randomised to fludrocortisone or its placebo. Primary outcome will be 90-day death or persistent organ dysfunction. Large simulation study will be performed across a range of plausible scenarios to foresee power to detect a 5%-10% absolute difference with corticosteroids. We will assess subset-by-treatment interaction by estimating in a Bayesian framework two quantities: (1) measure of influence, relying on the value of the estimation of corticosteroids' effect in each subset, and (2) measure of interaction. ETHICS AND DISSEMINATION: Dijon, France), on 6 April 2020. Trial results will be disseminated at scientific conferences and results will be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov Registry (NCT04280497).
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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 | 0.036 | 0.043 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.084 | 0.020 |
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".