Impact of clopidogrel use on mortality and major bleeding in patients undergoing coronary artery bypass surgery☆
Bibliographic record
Abstract
Patients who received clopidogrel prior to coronary bypass surgery are at increased risk for bleeding that must be balanced with risk of ongoing ischemia if coronary artery bypass grafting is delayed. This study aimed to evaluate the impact of clopidogrel on mortality and major bleeding in patients undergoing urgent coronary bypass surgery. We reviewed 451 consecutive patients who underwent urgent isolated coronary bypass surgery; 262 had not received clopidogrel, whereas 189 received clopidogrel < or = 5 days preoperative. The primary endpoint was in-hospital death, massive transfusion or massive blood loss. Patient characteristics were almost similar between groups. There was no difference in in-hospital death or massive bleeding indices between groups (clopidogrel: 7% vs. no clopidogrel: 6%, P = 0.9). No difference was observed even after adjusting for the date of stopping clopidogrel preoperatively. Multivariate regression analysis showed that clopidogrel or the duration it was stopped preoperatively, did not predict adverse outcomes. Significant independent predictors included preoperative renal dysfunction, hemoglobin level and peripheral vascular disease. clopidogrel, or the time it was stopped prior to surgery, was not a risk factor for in-hospital death, massive bleeding, or other poor early outcomes in patients undergoing urgent coronary artery bypass surgery.
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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 | 0.000 | 0.003 |
| 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 | 0.001 | 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".