Effectiveness of Aspirin on Major COPD Outcomes: A Prevalent New-User Design Observational Study
Bibliographic record
Abstract
Observational studies that have reported an association between aspirin use in chronic obstructive pulmonary disease (COPD) with reductions in mortality and COPD exacerbations were shown to be affected by time-related biases. We assessed this association using a prevalent new-user study design that avoids these biases. We used the United Kingdom's Clinical Practice Research Datalink (CPRD) to form a cohort of patients with COPD. Aspirin initiators were matched on time and propensity score with nonusers during 2002-2018. The outcomes were all-cause mortality and COPD exacerbation within a one-year follow-up. Hazard ratios (HR) and 95% confidence interval (CI) of each outcome associated with aspirin use compared to nonuse were estimated using an as-treated approach. The study cohort included 10,287 initiators of aspirin and 10,287 matched nonusers. The cumulative incidence of all-cause mortality at one year was 11.5% for aspirin users and 9.2% for nonusers. The HR of all-cause mortality associated with aspirin initiation was 1.22 (95% CI: 1.08-1.37), while for severe exacerbation it was 1.21 (95% CI 1.08-1.37), compared with nonuse. The HR of a first moderate or severe exacerbation with aspirin use was 0.90 (95% CI 0.85-0.95). These estimates did not vary by platelet count. This large population-based study, designed to emulate a trial, found aspirin use in patients with COPD associated with a higher risk of all-cause mortality and severe exacerbation, but a lower risk of moderate or severe exacerbation. Further research is warranted to assess this reduction in moderate or severe exacerbations, particularly in patients with cardiovascular risk factors.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".