Absolute benefit measures are essential for guiding therapies in COPD in an era of precision medicine: a viewpoint on “number needed to treat”
Bibliographic record
Abstract
Extract Chronic obstructive pulmonary disease (COPD) affects more than 380 million persons worldwide and is the 3rd leading cause of mortality [1, 2]. Most of the morbidity and mortality occur during periods of exacerbations, which are characterized by significant worsening of respiratory symptoms that typically lead to intensification of treatment with bronchodilators, antibiotics, or systemic corticosteroids [3]. Registration agencies such as the US Food and Drug Administration use findings from large Phase III trials, powered on exacerbations and other endpoints such as lung function or health-related quality of life, for approval of new therapeutics for COPD [4]. Notably, healthcare professionals rate exacerbation reduction as the single most important endpoint in their choice of a COPD drug [5].
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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 | 0.164 | 0.298 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".