Delayed time to first and subsequent exacerbations independent of season with indacaterol/glycopyrronium (IND/GLY) compared with salmeterol/fluticasone (SFC): the FLAME study
Bibliographic record
Abstract
Introduction: Seasonal differences have been identified in the pattern of COPD exacerbations.1 We compared the efficacy of IND/GLY vs SFC in reducing exacerbations across seasons and in reducing the risk of multiple exacerbations in patients (pts) with moderate-to-very severe COPD. Methods: FLAME, a 52-week, double-blind, double-dummy study, randomised (1:1) pts (with past year history of ≥1 exacerbation) to IND/GLY 110/50 µg o.d. or SFC 50/500 µg b.i.d.2 Here, we assessed the rate of moderate/severe exacerbation, and time to first and subsequent exacerbations. Results: Of 3362 pts randomised, 82.2% completed 52 weeks of study treatment. IND/GLY significantly reduced the rate of moderate/severe exacerbation in both spring/summer and fall/winter seasons vs SFC (Table). IND/GLY significantly delayed the time to 1st, 2nd, and 3rd exacerbation (moderate/severe), with a 22% (95% CI, 14-30%; P<0.001), 24% (95% CI, 11-34%; P<0.001), and 29% (95% CI, 9-44%; P=0.006) reduction in risk vs SFC, respectively. Conclusion: IND/GLY was more effective than SFC in reducing COPD exacerbations in both seasons and should be considered as the preferred first-line treatment option in COPD patients at high risk of exacerbations. References 1. Donaldson GC, et al. Int J COPD. 2014;9:1101-1110 2. Wedzicha JA, et al. NEJM. 2016;374:2222-2234
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".