Defining High Bleeding Risk in Patients Undergoing Percutaneous Coronary Intervention
Bibliographic record
Abstract
Identification and management of patients at high bleeding risk undergoing percutaneous coronary intervention are of major importance, but a lack of standardization in defining this population limits trial design, data interpretation, and clinical decision-making. The Academic Research Consortium for High Bleeding Risk (ARC-HBR) is a collaboration among leading research organizations, regulatory authorities, and physician-scientists from the United States, Asia, and Europe focusing on percutaneous coronary intervention-related bleeding. Two meetings of the 31-member consortium were held in Washington, DC, in April 2018 and in Paris, France, in October 2018. These meetings were organized by the Cardiovascular European Research Center on behalf of the ARC-HBR group and included representatives of the US Food and Drug Administration and the Japanese Pharmaceuticals and Medical Devices Agency, as well as observers from the pharmaceutical and medical device industries. A consensus definition of patients at high bleeding risk was developed that was based on review of the available evidence. The definition is intended to provide consistency in defining this population for clinical trials and to complement clinical decision-making and regulatory review. The proposed ARC-HBR consensus document represents the first pragmatic approach to a consistent definition of high bleeding risk in clinical trials evaluating the safety and effectiveness of devices and drug regimens for patients undergoing percutaneous coronary intervention.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".