Myocardial infarction and gastro-intestinal bleeding risks associated with aspirin use among elderly individuals with type 2 diabetes
Bibliographic record
Abstract
INTRODUCTION: The benefit of aspirin in primary prevention of myocardial infarction and the associated gastro-intestinal bleeding risks have not been well established in the elderly population with diabetes. METHODS: Using Quebec administrative databases, we conducted two nested case-control analyses within a cohort of individuals aged ≥ 66 years newly treated with an oral antidiabetes drug between 1998 and 2003. The 28,067 individuals had no cardiovascular disease recorded in the database in the year prior cohort entry. They had not used prescribed aspirin, antiplatelet, or anticoagulant drugs, and were not hospitalized for gastro-intestinal bleeding in the year prior cohort entry. The odds of myocardial infarction and gastro-intestinal bleedings were compared between individuals who were current, past, or non-users of aspirin. RESULTS: There were 1101 (3.9%) cases of myocardial infarction. Compared to non-users, neither aspirin users (OR 0.89; 95% CI 0.71-1.13) nor aspirin past users (0.81; 0.62-1.06) showed a statistically significant lower risk of myocardial infarction. There were 373 (1.3%) cases of gastro-intestinal bleeding. Current users of aspirin had about a 2-fold greater risk of gastro-intestinal bleeding compared to non-users (2.19; 1.53-3.13). CONCLUSIONS: Our results suggest that individual assessment of bleeding risk and cardiovascular risk is mandatory among elderly people with diabetes before introducing aspirin therapy.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".