Aversion to ambiguity and willingness to take risks affect therapeutic decisions in managing atrial fibrillation for stroke prevention: results of a pilot study in family physicians
Bibliographic record
Abstract
BACKGROUND: Anticoagulation is the therapeutic paradigm for stroke prevention in patients with atrial fibrillation (AF). It is unknown how physicians make treatment decisions in primary stroke prevention for patients with AF. OBJECTIVES: To evaluate the association between family physicians' risk preferences (aversion risk and ambiguity) and therapeutic recommendations (anticoagulation) in the management of AF for primary stroke prevention by applying concepts from behavioral economics. METHODS: Overall, 73 family physicians participated and completed the study. Our study comprised seven simulated case vignettes, three behavioral experiments, and two validated surveys. Behavioral experiments and surveys incorporated an economic framework to determine risk preferences and biases (e.g., ambiguity aversion, willingness to take risks). The primary outcome was making the correct decision of anticoagulation therapy. Secondary outcomes included medical errors in the management of AF for stroke prevention. RESULTS: Overall, 23.3% (17/73) of the family physicians elected not to escalate the therapy from antiplatelets to anticoagulation when recommended by best practice guidelines. A total of 67.1% of physicians selected the correct therapeutic options in two or more of the three simulated case vignettes. Multivariate analysis showed that aversion to ambiguity was associated with appropriate change to anticoagulation therapy in the management of AF (OR 5.48, 95% CI 1.08-27.85). Physicians' willingness to take individual risk in multiple domains was associated with lower errors (OR 0.16, 95% CI 0.03-0.86). CONCLUSION: Physicians' aversion to ambiguity and willingness to take risks are associated with appropriate therapeutic decisions in the management of AF for primary stroke prevention. Further large scale studies are needed.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".