A treatment decision aid may increase patient trust in the diabetes specialist. The <i>Statin Choice</i> randomized trial
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Bibliographic record
Abstract
AIMS: Decision aids in practice may affect patient trust in the clinician, a requirement for optimal diabetes care. We sought to determine the impact of a decision aid to help patients with diabetes decide about statins (Statin Choice) on patients' trust in the clinician. METHODS: We randomized 16 diabetologists and 98 patients with type 2 diabetes referred to a subspecialty diabetes clinic to use the Statin Choice decision aid or a patient pamphlet about dyslipidaemia, and then to receive these materials from either the clinician during the visit or a researcher prior to the visit. Providers and patients were blinded to the study hypothesis. Immediately after the clinical encounter, patients completed a survey including questions on trust (range 0 to total trust = 100), knowledge, and decisional conflict. Researchers reviewed videotaped encounters and assessed patient participation (using the OPTION scale) and visit length. RESULTS: Overall mean trust score was 91 (median 97.2, IQR 86, 100). After adjustment for patient characteristics, results suggested greater total trust (trust = 100) with the decision aid [odds ratio (OR) 1.77, 95% CI 0.94, 3.35]. Total trust was associated with knowledge (for each additional knowledge point, OR 1.3, 95% CI 1.1, 1.6), patient participation (for each additional point in the OPTION scale, OR 1.1, 95% CI 1.1, 1.2), and decisional conflict (for every 5-point decrease in conflict, OR 1.5, 95% CI 1.2, 1.9). Total trust was not associated with visit length, which the decision aid did not significantly affect. There was no significant effect interaction across the trial factors. CONCLUSIONS: Preliminary evidence suggests that decision aids do not have a large negative impact on trust in the physician and may increase trust through improvements in the decision-making process.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it