Time to first moderate or severe COPD exacerbation with tezepelumab (COURSE)
Bibliographic record
Abstract
Background: In COURSE ( NCT04039113 ), tezepelumab numerically reduced the annualized rate of moderate or severe COPD exacerbations (overall by 17% [p=0.1042]; in patients with a blood eosinophil count [BEC] ≥150 cells/µL by 37%) and severe COPD exacerbations by 48% vs placebo over 52 weeks. Objective: Assess the efficacy of tezepelumab in delaying moderate or severe COPD exacerbations. Methods: COURSE was a phase 2a, randomized, double-blind, placebo-controlled study. Patients (≥40–80 years) with moderate to very severe COPD were randomized 1:1 to tezepelumab 420 mg or placebo subcutaneously every 4 weeks for up to 52 weeks. Eligible patients were receiving triple inhaled maintenance therapy and had ≥2 moderate or severe COPD exacerbations in the 12 months before enrolment. Time to first moderate or severe COPD exacerbation, and the proportions of patients with ≥1 of each, were assessed. Results: Of 333 randomized and treated patients (tezepelumab, n=165; placebo, n=168), 41%, 59% and 17% had baseline BECs <150, ≥150 and ≥300 cells/µL, respectively. Tezepelumab delayed time to first moderate or severe exacerbation vs placebo in the overall population (HR, 0.80 [95% CI: 0.61–1.06]; median days: 253 vs 214) and in those with baseline BECs <150 (HR, 0.88 [95% CI: 0.57–1.38]), ≥150 (HR, 0.74 [95% CI: 0.51–1.07]) and ≥300 cells/µL (HR, 0.51 [95% CI: 0.24–1.04]). Overall, time to first severe exacerbation was also delayed vs placebo (HR, 0.70 [95% CI: 0.36–1.33]). Fewer tezepelumab than placebo recipients had ≥1 moderate or severe exacerbation (57% vs 63%) or ≥1 severe exacerbation (10% vs 13%). Conclusion: Tezepelumab delayed the time to first moderate or severe COPD exacerbation, overall and across BEC subgroups.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".