Should Clinicians Deliver Decision Aids? Further Exploration of the Statin Choice Randomized Trial Results
Bibliographic record
Abstract
BACKGROUND: Statin Choice is a decision aid about taking statins. The optimal mode of delivering Statin Choice (or any other decision aid) in clinical practice is unknown. METHODS: To investigate the effect of mode of delivery on decision aid efficacy, the authors further explored the results of a concealed 2 x 2 factorial clustered randomized trial enrolling 21 endocrinologists and 98 diabetes patients and randomizing them to 1) receive either the decision aid or pamphlet about cholesterol, and 2) have these delivered either during the office visit (by the clinician) or before the visit (by a researcher). We estimated between-group differences and their 95% confidence intervals (CI) for acceptability of information delivery (1-7), knowledge about statins and coronary risk (0-9), and decisional conflict about statin use (0-100) assessed immediately after the visit. Follow-up was 99%. RESULTS: The relative efficacy of the decision aid v. pamphlet interacted with the mode of delivery. Compared with the pamphlet, patients whose clinicians delivered the decision aid during the office visit showed significant improvements in knowledge (difference of 1.6 of 9 questions, CI 0.3, 2.8) and nonsignificant trends toward finding the decision aid more acceptable (odds ratio 3.1, CI 0.9, 11.2) and having less decisional conflict (difference of 7 of 100 points, CI -4, 18) than when a researcher delivered the decision aid just before the office visit. CONCLUSIONS: Delivery of decision aids by clinicians during the visit improves knowledge and shows a trend toward better acceptability and less decisional conflict.
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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 | 0.207 | 0.361 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".