Risk of ST versus non-ST elevation myocardial infarction associated with non-steroidal anti-inflammatory drugs
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to explore the association of non-steroidal anti-inflammatory drugs (NSAID) with ST-segment elevation myocardial infarction (STEMI) and non-ST segment elevation myocardial infarction (NSTEMI). DESIGN, SETTING & PATIENTS: A matched case-control study comparing patients with incident non-fatal myocardial infarction (MI) collected by cardiologists with controls. Cases were retrieved from the Pharmacoepidemiological General Research on Myocardial Infarction (PGRx-MI) registry, a French nationwide registry consisting of 55 cardiology centres, whereas controls were selected from general practice settings. Both cases and controls were recruited from the same geographically diverse areas across continental France. MAIN OUTCOME MEASURES: The association between NSAID and MI was assessed by matched adjusted OR from conditional logistic regression. RESULTS: Between 2007 and 2009, 1125 incident cases were included (67.3% and 32.7% for STEMI and NSTEMI, respectively), with 2790 controls matched to MI cases by age and sex. Current use (previous 2 months) of either diclofenac or naproxen and other arylpropionic acid NSAID was not associated with STEMI (OR 0.9, 95% CI 0.4 to 1.9 and OR 1.0, 95% CI 0.6 to 1.7, respectively), instead it showed significant association with NSTEMI (OR 2.8, 95% CI 1.2 to 6.4 and OR 0.4, 95% CI 0.2 to 0.9, respectively). Our study confirms results from previously published analyses on the association of MI with NSAID (OR 1.5, 0.9, and 1.0 for diclofenac, naproxen and related NSAID, and all NSAID combined, respectively). CONCLUSIONS: Our study shows that the MI risk modification associated with NSAID is limited to NSTEMI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".