Optimal antiplatelet and antithrombotic regimen post‐transcatheter aortic valve replacement
Bibliographic record
Abstract
BACKGROUND: There are limited data regarding optimal antiplatelet/antithrombotic therapy following transcatheter aortic valve replacement (TAVR). METHODS: In this single-centre retrospective study including TAVR patients from 2012 to 2020, ischemic and bleeding outcomes were compared between antiplatelet (dual antiplatelet [DAPT] vs. single antiplatelet [SAPT]) and oral anticoagulation (OAC) groups using incidence rate, Kaplan-Meier and Cox proportional hazards analysis. RESULTS: Total 492 patients (mean age 79.7 ± 7.7 years, 53.7% males, 83.5% Caucasian) were included. There was higher incidence of 1-year death or ischemia with DAPT vs. SAPT (23.6 vs. 14.8 per 100 patient-years [PY], incidence rate ratio [IRR] 1.60, 95% confidence interval [CI] 0.97-2.68, p = .05), especially in those without coronary artery disease (23.9 vs. 10.7 per 100 PY, IRR 2.24, 95% CI 1.10-4.47, p = .017). There was significantly higher major bleeding in those on OAC vs. no OAC (15 vs. 8 per 100 PY, IRR 1.87, 95% CI 1.10-3.11, p = .016), especially late (>1-year) bleeding (10.2 vs. 3.6 per 100 PY, IRR 2.81, 95% CI 1.33-5.92, p = .004). In multivariate analysis, DAPT was an independent predictor of death or ischemia (adjusted hazard ratio [aHR] 1.41, 95% CI 1.01-1.96, p = .041). OAC was an independent predictor of major bleeding (aHR 2.32, 95% CI 1.31-4.13, p = .004). CONCLUSIONS: There is signal to harm with routine use of DAPT post-TAVR. There is higher incidence of late bleeding post-TAVR with OAC, suggesting potential role for alternate antithrombotic strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".