Prevention and treatment of bleeding complications in patients receiving vitamin K antagonists, Part 1: Prevention
Bibliographic record
Abstract
Oral anticoagulant therapy with coumarins is widely \nused for the prevention and treatment of venous and \narterial thrombosis. The most common complication of \nvitamin K antagonist therapy is bleeding, with major \nbleeding events occurring in 1–3% of patients annually. \nAlthough a number of potential predictors for bleeding \nhave been described, little research is available to provide \nevidence-based guidelines for the prevention of \nbleeding. To address this knowledge gap, we assembled \na panel of international experts and posed a series of \nfocused clinical questions. The experts were asked to \nperform a systematic literature review and summarize the \nresults of that review within the context of their clinical \nquestion. In many cases, data were lacking and the \nexperts were asked to supplement their answer with clinical \nexpertise. To minimize bias the reviews were vetted \nby three internationally recognized scholarly bodies. Our \ngoal in this project is to provide ‘‘best evidence’’ for \nclinicians faced with the problem of minimizing the risk \nof bleeding in patients on vitamin K antagonist therapy
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".