Association Between Naproxen Use and Protection Against Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: The association between the use of nonsteroidal anti-inflammatory drugs (NSAIDs) and acute myocardial infarction (AMI) is unclear. Nonsteroidal anti-inflammatory drugs vary in their antithrombotic properties, with naproxen having a particularly effective antithrombotic potential. OBJECTIVE: To compare the effect of naproxen vs other NSAIDs in the prevention of AMI in an older population. METHODS: Population-based, matched case-control study. Patients (aged > or =65 years) in Quebec had been hospitalized for AMI between January 1, 1992, and December 31, 1994. The admission date for AMI was considered the index date. Control subjects were randomly selected from a Quebec drug and physician claims database. For each case, a control was matched with the same index date, age (within 2 years), and sex. Cases and controls were required to have at least 1 year of pharmaceutical and medical records before the index date to identify risk factors for AMI and exposure to naproxen or other nonaspirin NSAIDs. Concurrent exposure to a medication was defined as exposure to that medication at the index date. Logistic regression analyses were used to evaluate the association between the use of naproxen and other NSAIDs in the prevention of AMI, adjusting for potential confounders. RESULTS: Included in the study were 4163 cases and 14 160 controls. Determinants (adjusted odds ratios [95% confidence intervals]) of AMI included use in the prior year of anticoagulants (0.76 [0.64-0.90]), nitrates (2.01 [1.86-2.17]), antidiabetic agents (1.72 [1.56-1.90]), antihypertensive agents (1.36 [1.28-1.45]), and lipid-lowering agents (0.83 [0.75-0.91]), as well as concurrent exposure to naproxen vs other NSAIDs (0.79 [0.63-0.99]). CONCLUSION: Compared with other NSAIDs, concurrent exposure to naproxen has a protective effect against AMI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".