Ibuprofen may abrogate the benefits of aspirin when used for secondary prevention of myocardial infarction.
Bibliographic record
Abstract
OBJECTIVE: To determine whether patients taking aspirin for secondary prevention of myocardial infarction are at increased risk of recurrent disease when they take concomitant ibuprofen. METHODS: In this population based, retrospective cohort study using governmental databases, patients > or = 66 years of age, hospitalized for an index acute myocardial infarction (AMI) between January 1992 and March 1999 and taking ASA throughout the period of followup were identified. The main exposure was the concomitant use of ibuprofen and ASA after the index AMI. The outcome of interest was recurrent AMI. Subjects were followed to one year after the index AMI. RESULTS: A total of 18,503 patients met the study entry criteria. Of these, 372 patients were dispensed a prescription for ibuprofen (exposed) and 14,424 patients were not dispensed a prescription for any nonsteroidal antiinflammatory drug (NSAID) (unexposed). Patients dispensed prescriptions for any NSAID (n = 4079), naproxen (n = 1239), and diclofenac (n = 1474) were analyzed separately. There was a trend to an increase in the rate of recurrent AMI in patients taking ibuprofen and ASA compared to those taking ASA alone as the duration of exposure increased [hazard ratios for ever, > or = 30 days, and > or = 60 days exposed were 1.01 (95% CI 0.58-1.76), 1.13 (95% CI 0.54-2.39), and 1.83 (95% CI 0.76-4.42), respectively]. In contrast, subjects taking prolonged naproxen and ASA had a trend toward a lower rate of recurrent AMI compared to those taking ASA alone. CONCLUSION: The results are consistent with data that suggest that regular, but not intermittent, ibuprofen may abrogate the benefits of aspirin when used for the secondary prevention of AMI. There may be differences in the risk of heart disease with various NSAID.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".