Cardiovascular Safety of Celecoxib in Acute Myocardial Infarction Patients: A Nested Case-Control Study
Bibliographic record
Abstract
The objective was to measure the impact of exposure to coxibs and non-steroidal antiinflammatory drugs (NSAID) on morbidity and mortality in older patients with acute myocardial infarction (AMI). A nested case-control study was carried out using an exhaustive population-based cohort of patients aged 66 years and older living in Quebec (Canada) who survived a hospitalization for AMI (ICD-9 410) between 1999 and 2002. The main variables were all-cause and cardiovascular (CV) death, subsequent hospital admission for AMI, and a composite end-point including recurrent AMI or CV death. Conditional logistic regressions were used to estimate the risk of mortality and morbidity. A total of 19,823 patients aged 66 years and older survived hospitalization for AMI in the province of Quebec between 1999 and 2002. After controlling for covariables, the risk of subsequent AMI and the risk of composite end-point were increased by the use of rofecoxib. The risk of subsequent AMI was particularly high for new rofecoxib users (HR 2.47, 95% CI 1.57-3.89). No increased risk was observed for celecoxib users. No increased risk of CV death was observed for patients exposed to coxibs or NSAIDs. Patients newly exposed to NSAIDs were at an increased risk of death (HR 2.22, 95% CI 1.30-3.77) and of composite end-point (HR 2.28, 95% CI 1.35-3.84). Users of rofecoxib and NSAIDs, but not celecoxib, were at an increased risk of recurrent AMI and of composite end-point. Surprisingly, no increased risk of CV death was observed. Further studies are needed to better understand these apparently contradictory results.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".