Clinician's use of the <i>Statin Choice</i> decision aid in patients with diabetes: a videographic study nested in a randomized trial
Bibliographic record
Abstract
OBJECTIVE: To describe how clinicians use decision aids. BACKGROUND: A 98-patient factorial-design randomized trial of the Statin Choice decision vs. standard educational pamphlet; each participant had a 1:4 chance of receiving the decision aid during the encounter with the clinician resulting in 22 eligible encounters. DESIGN: Two researchers working independently and in duplicate reviewed and coded the 22 encounter videos. SETTING AND PARTICIPANTS: Twenty-two patients with diabetes (57% of them on statins) and six endocrinologists working in a referral diabetes clinic randomly assigned to use the decision aid during the consultation. MAIN OUTCOME MEASURES: Proportion and nature of unintended use of the Statin Choice decision aid. RESULTS: We found eight encounters involving six clinicians who did not use the decision aid as intended either by not using it at all (n = 5; one clinician did use the decision aid in three encounters), offering inaccurate quantitative and probabilistic information about the risks and benefits of statins (n = 2), or using the decision aid to advance the agenda that all patients with diabetes should take statin (n = 1). Clinicians used the decision aid as intended in all other encounters. CONCLUSIONS: Unintended decision aid use in the context of videotaped encounters in a practical randomized trial was common. These instances offer insights to researchers seeking to design and implement effective decision aids for use during the clinical visit, particularly when clinicians may prefer to proceed in ways that the decision aid apparently contradicts.
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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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".