Validation of a patient decision aid for choosing between dabigatran and warfarin for atrial fibrillation.
Bibliographic record
Abstract
BACKGROUND: Decision aids have been helpful to support patients in decision-making including anticoagulation. With the introduction of new oral anticoagulants (NOACs), it will be important to assist patients and physicians in shared decision-making about NOACs and warfarin. OBJECTIVES: To validate a patient decision aid (DA) for warfarin versus dabigatran, the first NOAC approved for atrial fibrillation (AF). METHODS: Participants without AF and not taking anticoagulants were recruited for the validation exercise. The decision aid described AF, stroke, and hemorrhagic events in terms of incidence, clinical presentation, and prognosis. Warfarin and dabigatran were then compared on multiple clinical and process outcomes as outlined in the pivotal clinical trial. Our primary outcome was confidence in making a treatment decision, using a decisional conflict scale. Secondary outcomes were change in knowledge scores and ratings of clarity, helpfulness and comprehensiveness. RESULTS: 35 patients (mean age 62.7 [SD 9.68], 37.1% female) participated. After use of the decision aid, the mean total decisional conflict score was low at 18.9 (SD: 14.2). Mean knowledge score improved significantly from 4.60 (SD 1.48) to 6.42 (SD 0.80) out of a total score of 7. Only one participant (2.9%) found the decision aid difficult to understand. All 35 participants rated the DA as helpful for making a decision about anticoagulant treatment for AF. Two participants (5.7%) requested more information on adverse effects of the two drugs. CONCLUSION: Our DA to allow patients to make an informed decision with their physician regarding dabigatran versus warfarin in AF, proved understandable, comprehensive and helpful.
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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 | 0.020 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".