Values and preferences for oral antithrombotic therapy in patients with atrial fibrillation: physician and patient perspectives
Bibliographic record
Abstract
BACKGROUND: Exploration of values and preferences in the context of anticoagulation therapy for atrial fibrillation (AF) remains limited. To better characterize the distribution of patient and physician values and preferences relevant to decisions regarding anticoagulation in patients with AF, we conducted interviews with patients at risk of developing AF and physicians who manage patients with AF. METHODS: We interviewed 96 outpatients and 96 physicians in a multicenter study and elicited the maximal increased risk of bleeding (threshold risk) that respondents would tolerate with warfarin vs. aspirin to achieve a reduction in three strokes in 100 patients over a 2-year period. We used the probabilistic version of the threshold technique. RESULTS: The median threshold risk for both patients and physicians was 10 additional bleeds (10 P = 0.7). In both groups, we observed large variability in the threshold number of bleeds, with wider variability in patients than clinicians [patient range: 0-100, physician range: 0-50]. We observed one cluster of patients and physicians who would tolerate <10 bleeds and another cluster of patients, but not physicians, who would accept more than 35. CONCLUSIONS: Our findings suggest wide variability in patient and physician values and preferences regarding the trade-off between strokes and bleeds. Results suggest that in individual decision making, physician and patient values and preferences will often be discordant; this mandates tailoring treatment to the individual patient's preferences.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".