Abstract 18402: Impact of the Use of Different Bleeding Scales on the Rate of 6-Month Bleeding After PCI: Results from the PARIS Registry
Bibliographic record
Abstract
Background: Multiple definitions have been used to classify bleeding, which is an important safety metric in cardiovascular clinical studies. We investigated the impact of different scales on apparent 6-month bleeding rates in a real-world registry of patients on dual antiplatelet therapy following percutaneous coronary intervention (PCI). Methods: PARIS is an ongoing multicenter, multinational, observational study of 5033 patients examining the modes and clinical correlates of medication non-adherence. All bleeding events were independently adjudicated according to the TIMI (Thrombolysis in Myocardial Infarction), BARC (Bleeding Academic Research Consortium) and ACUITY Acute Catheterization and Urgent Intervention Triage Strategy) bleeding classification schemes. In this analysis, we compared the bleeding rates using these bleeding scales. BARC <3 bleeding was considered minor; BARC ≥3 bleeding was considered major. Results: The overall incidence of minor and major bleeding at 6 months using the ACUITY classification scheme was 4.71% (n=237).; overall bleeding rates defined by the TIMI, and BARC scales were 1.21% and 4.67%, respectively (Figure). Rates of minor bleeding were 0.5%, 3.28%, and 3.18% using the TMI, BARC and ACUITY definitions; rates of major bleeding were 0.74%, 1.49% and 1.65%, respectively. Agreement between the ACUITY and BARC definitions yielded a high level of correlation (kappa 0.86, p< 0.001). TIMI bleeding, rates were excluded from this analysis do the low number of TIMI bleeds. Conclusions: In this real-world contemporary PCI registry, the overall incidence of any, major and minor bleeding at 6 months varied substantially between different classification schemes. These findings highlight the importance of bleeding definition selection and emphasize the problem of cross trial comparison when different definitions of bleeding are used.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".