Cochrane corner: self-monitoring and self-management of oral anticoagulation
Bibliographic record
Abstract
Use of oral anticoagulants such as warfarin is increasing. Part of the reason for this is the rising prevalence of atrial fibrillation, an ageing population and the widening indications for treatment based on evidence of benefit in reducing risk of stroke. A meta-analysis of 29 randomised trials including 28 044 participants with atrial fibrillation found that warfarin decreased the absolute risk of stroke by 2.7% per year (the number needed to treat (NNT) 37) compared with placebo or no treatment and by 0.7% per year (NNT=143) compared with aspirin.1 Management of warfarin, however, is challenging because of the considerable variability in warfarin’s action and the narrow ‘therapeutic range’, which requires frequent testing of international normalised ratio (INR) values and appropriate adjustment to prevent major complications. Often, poor control means that much of the potential benefit is not realised. Point-of-care devices, which allow self-testing of INR, with a drop of whole blood, are one of the options to optimise management by potentially reducing the need to attend anticoagulation clinics and offering the possibility for more continuous measurement.2 The first randomised trial of patient self-testing, published in 1989, included 50 patients on warfarin but with poorly controlled INRs, found that self-testing in the home setting provided accurate measurements, was feasible and achieved superior control compared with standard anticoagulation clinic care.3 Over time, there …
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.107 | 0.011 |
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".