Abstract 16441: Stent Thrombosis and Major Bleeding With Bivalirudin versus Active Control in Patients Undergoing Percutaneous Coronary Intervention: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Introduction: Although bivalirudin is commonly used for patients undergoing percutaneous coronary intervention (PCI), there has been recent concern with regard to stent thrombosis and major bleeding with bivalirudin versus active control. Hypothesis: We assessed the hypothesis that whether there is a significant difference between bivalirudin and active control with regard to the risk of stent thrombosis and major bleeding. Methods: We searched PubMed, clinicaltrials.gov, and conference proceedings for randomized controlled trials of bivalirudin versus active control in patients undergoing PCI. The co-primary endpoints were definite stent thrombosis and major bleeding. We used Mantel-Haenszel fixed-effects model to report risk ratio (RR) with 95% confidence interval (CI) for the pooled data. Random-effects model was used in case of heterogeneity (I2>0%). Results: We included 24 trials involving 39,049 patients. Use of bivalirudin compared with active control was associated with an increase in the risk of definite stent thrombosis (11 trials; 16,864 patients; RR, 1.80; 95% CI, 1.31-2.48; P Conclusions: Compared with active control, bivalirudin is associated with increased risk of acute stent thrombosis but lower risk of major bleeding.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.017 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".