The risk of bleeding with warfarin: A systematic review and performance analysis of clinical prediction rules
Bibliographic record
Abstract
It was the objective of this article to qualitatively review and evaluate the clinical prediction rules (CPRs) available for estimating bleeding risk in patients commencing warfarin therapy. A systematic review of PubMed (1949 to December 2006), MEDLINE (1966 to December 2006); EMBASE (1980 to December 2006), Cochrane Database of Systematic Reviews (to December 2006), and International Pharmaceutical Abstracts (1970 to December 2006) was conducted. Seven studies were found that detailed CPRs used to assess risk of bleeding prior to commencing warfarin therapy. Four studies described distinct CPRs. The remaining three studies were further validations of one of the CPRs, the Outpatient Bleeding Risk Index. The Outpatient Bleeding Risk Index was classified as being of Level 2 evidence while the remaining three indices were classified as being of Level 4 evidence. In no case did the CPRs exhibit performance characteristics that would indicate"strong" ability to predict the presence of absence of major bleeding among warfarin recipients. The modified Outpatient Bleeding Risk Index exhibited moderate predictive ability for major bleeding in two studies, although pooling of all studies of this CPR did not reveal moderate or better performance. None of the CPRs identified "any bleeding" with moderate or strong predictive ability. None of the available CPRs exhibit sufficient predictive accuracy or have trials evaluating the impact of their use on patient outcomes. Hence, no existing CPR can be recommended for widespread use in practice at present.
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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 | 0.036 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".