Win ratio analysis of dupilumab efficacy in patients with chronic obstructive pulmonary disease and type 2 inflammation: BOREAS and NOTUS
Bibliographic record
Abstract
Background: Evaluation of multiple endpoints in clinical trials is often limited by endpoints of lower clinical importance. Win ratios are used to categorize multiple outcomes by clinical importance. Aims & Objectives: This post hoc analysis uses win ratios to compare dupilumab efficacy vs placebo in BOREAS and NOTUS. Methods: BOREAS ( NCT03930732 ) and NOTUS ( NCT04456673 ), phase 3 randomized, double-blind, placebo-controlled trials, enrolled patients with COPD, and type 2 inflammation (screening blood eosinophil count ≥300 cells/µL) on LABA/LAMA/ICS. Patients received add-on dupilumab 300 mg or placebo q2w for 52 weeks. Win ratio analysis compared each patient on dupilumab to each patient on placebo for time-to-event and occurrence-of-event. Endpoints were ordered based on clinical priority. Results: Evaluation of the pooled population (dupilumab n=938; placebo n=934) across multiple endpoints for occurrence-of-event showed patients receiving dupilumab being 32% more likely to avoid hierarchically important clinical deterioration compared with placebo (1.32; 95% CI 1.17, 1.49]) (Figure). Similar findings were seen for the time-to-event endpoints (win ratio 1.26; 95% CI 1.13, 1.40). Conclusions: Dupilumab vs placebo increased the odds of avoiding hierarchically worse clinical outcomes by 32% in patients with COPD and type 2 inflammation. erj;66/suppl_69/PA4572/F1 F1 F1
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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 | 0.048 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.014 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".