Balancing the Risks of Thrombosis and Bleeding Following Transcatheter Aortic Valve Implantation: Current State-of-Evidence
Bibliographic record
Abstract
While transcatheter aortic valve implantation (TAVI) has rapidly evolved as an acceptable alternative to conventional surgical aortic valve replacement in elderly, high-risk surgical candidates with critical aortic stenosis, thrombotic and bleeding complications remain relatively frequent and potentially life-threatening. Thrombotic events during and following TAVI relate to the dynamic interplay between the systemic burden of atherosclerotic disease, atrial arrhythmias, device and native aortic valve interactions, as well as platelet and coagulation cascade activation. Bleeding in the acute setting relates primarily to access site vascular complications, but also appears related to pre-existing renal impairment and anemia. Current pre-, peri- and post-procedural anti-thrombotic regimens are empirical, based on expert consensus following extrapolation from the wealth of experience gleaned following percutaneous coronary intervention. However the complexities of the TAVI procedure, the high-risk clinical substrate and competing effects of anti-thrombotic regimens and bleeding risk are yet to be prospectively assessed in randomized clinical trials for defining evidence-based anti-thrombotic 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".