A treatment decision aid may increase patient trust in the diabetes specialist. The <i>Statin Choice</i> randomized trial
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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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".