Defining high bleeding risk in patients undergoing transcatheter aortic valve implantation: a VARC-HBR consensus document
Bibliographic record
Abstract
The identification and management of patients at high bleeding risk (HBR) undergoing transcatheter aortic valve implantation (TAVI) are of major importance, but the lack of standardised definitions is challenging for trial design, data interpretation, and clinical decision-making. The Valve Academic Research Consortium for High Bleeding Risk (VARC-HBR) is a collaboration among leading research organisations, regulatory authorities, and physician-scientists from Europe, the USA, and Asia, with a major focus on TAVI-related bleeding. VARC-HBR is an initiative of the CERC (Cardiovascular European Research Center), aiming to develop a consensus definition of TAVI patients at HBR, based on a systematic review of the available evidence, to provide consistency for future clinical trials, clinical decision-making, and regulatory review. This document represents the first pragmatic approach to a consistent definition of HBR evaluating the safety and effectiveness of procedures, devices and drug regimens for patients undergoing TAVI..
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".