Effect of Long-Term Aspirin Use on Embolic Events in Infective Endocarditis
Bibliographic record
Abstract
BACKGROUND: In a recent clinical trial, aspirin therapy was initiated approximately 34 days after the onset of symptoms but did not reduce the risk of embolism in patients with endocarditis. However, it is possible that aspirin used early in the course of the disease may be beneficial. The purpose of the study is to assess the effect of long-term daily aspirin use on the risk of embolic events in patients with infective endocarditis. METHODS: The clinical characteristics and outcomes of patients excluded from the Multi-Centre Aspirin Trial in Infective Endocarditis because of long-term aspirin use (n = 84) were compared with the data for patients randomized to the placebo arm (n = 55). The former patients took aspirin before and during the early stages of infective endocarditis, whereas the latter patients were not exposed to aspirin before and during the entire hospitalization. Logistic modeling was used to assess the effect of long-term aspirin use on embolism and bleeding. RESULTS: There was a trend toward excess bleeding in long-term aspirin recipients, compared with placebo recipients (P = .065). Logistic modeling revealed that long-term aspirin use may be associated with excess bleeding (unadjusted odds ratio, 2.35 [P = .059]; adjusted odds ratio, 2.08 [P = .118]), but it had no impact on the risk of embolic events in either model. CONCLUSIONS: In patients with endocarditis, long-term daily use of aspirin does not reduce the risk of embolic events but may be associated with a higher risk of bleeding. In the acute phase of endocarditis, aspirin should be used with caution.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".