A randomized trial to assess the impact of an antithrombotic decision aid in patients with nonvalvular atrial fibrillation: the DAAFI trial protocol [ISRCTN14429643]
Bibliographic record
Abstract
BACKGROUND: Decision aids are often advocated as a means to assist patient and health care provider decision making when faced with complicated treatment or screening decisions. Despite an exponential growth in the availability of decision aids in recent years, their impact on long-term treatment decisions and patient adherence is uncertain due to a paucity of rigorous studies. The choice of antithrombotic therapy for nonvalvular atrial fibrillation (NVAF) is one condition for which a trade-off exists between the potential risks and benefits of competing therapies, and the need to involve patients in decision making has been clearly identified. This study will evaluate whether an evidence-based patient decision aid for patients with NVAF can improve the appropriateness of antithrombotic therapy use by patients and their family physicians. DESIGN: A multi-center, two-armed cluster randomized trial based in community family practices in which patients with NVAF will be randomized to decision aid or usual care. Patients will receive one of four decision aids depending on their baseline stroke risk. The primary outcome is the provision of "appropriate antithrombotic therapy" at 3 months to study participants (appropriateness defined as per the 2001 American College of Chest Physicians recommendations for NVAF). In addition, the impact of this decision aid on patient knowledge, decisional conflict, well-being, and adherence will be assessed after 3 months, 6 months, and 12 months.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".