Cochrane corner: self-monitoring and self-management of oral anticoagulation
Bibliographic record
Abstract
<p>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 randomized trials including 28,044 participants with atrial fibrillation found warfarin decreased the absolute risk of stroke by 2.7% per year (number needed to treat [NNT] 37) compared to placebo or no treatment, and by 0.7% per year (NNT = 143) when compared to aspirin.</p> <br/> <p>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 normalized 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 randomized 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 when compared with standard anticoagulation clinic care. [3] Over time there have been a number of further randomized controlled trials (RCTs) done to establish the effectiveness of selfmonitoring. In parallel, self-testing devices have generally proved to be reliable and analytically accurate.</p> <br/> <p>Trials that have evaluated self-monitoring usually adopt two types of self-monitoring models. In some, a (trained participant tests their INR test and informs their healthcare provider of the result. In others, there is a greater degree of self-management where a trained participant tests their INR, interprets the result, and adjusts the drug dosage accordingly. [5] Given the growing evidence base, we updated our systematic review of the impact of patient self-monitoring or self-management on treatment with oral anticoagulation therapy.</p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".