Aspirin Use in the Setting of Acute Myocardial Infarction and Peptic Ulcer Bleeding Does Not Increase the Rebleeding Rate
Bibliographic record
Abstract
Purpose: To determine if the use of aspirin or other anti-platelet agents is associated with adverse outcomes in patients with a bleeding peptic ulcer in the setting of acute myocardial infarction. Methods: Patients with a bleeding peptic ulcer and a concurrent acute myocardial infarction were evaluated from 2 tertiary care centers between 1999 and 2007. The pattern of aspirin (ASA) use was determined. Peptic ulcer rebleeding rates and cardiac outcomes were assessed. Results: 102 patients were evaluated retrospectively. Seventy nine patients (78%) received ASA and 46 patients (45%) received clopidogrel during an acute myocardial infarction. 34 patients (33%) were continued on ASA therapy after peptic ulcer bleeding whereas the remaining 68 patients (67%) had ASA held temporarily or discontinued during hospitalization. Patients who had ASA continued were more likely to have had a STEMI, IIb/IIIa inhibitor use, percutaneous coronary intervention and/or CABG (P < 0.05). There was no difference in the rebleeding rate from ulcers with low risk stigmata between patients who continued ASA compared to those who had ASA held or discontinued (4.0% vs 7.5%). Among patients with high risk stigmata, there was also no difference in the rebleeding rate (23% vs 29%). When ASA was continued, there was no increase in the rebleeding rate when clopidogrel was used or not (7% vs 11%). Mortality tended to be lower in patients who had ASA continued compared to those who had ASA held or discontinued (9% vs 16%). Conclusion: Aspirin does not appear to increase the rate rebleeding in patients presenting with peptic ulcer bleeding in the setting of an acute myocardial infarction.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".