Can Computerized Decision Support Help Patients Make Complex Treatment Decisions? A Randomized Controlled Trial of an Individualized Menopause Decision Aid
Bibliographic record
Abstract
PURPOSE: To compare the effectiveness of an individualized decision aid (DA) with standard educational materials on decisions about menopausal treatments and to assess the feasibility of integrating this DA into clinical practice, with and without coaching. METHODS: We conducted a 3-armed randomized controlled trial in 3 clinics, enrolling menopausal women between the ages of 45 and 65 years with primary care appointments. Of the 145 women included, 99 completed a 2-week follow-up. The control group received generic educational materials, 1 intervention group received an individualized computer-generated DA mailed to patients and their clinicians before clinic appointment, and the 2nd intervention group received the same DA along with coached care before clinic appointment (DA + CC). Decisional conflict, satisfaction, and knowledge were measured 2 weeks after clinic appointment. RESULTS: Participants' mean age was 52 years, and 97% were white. Most women (98%) read all or most of the documents. Decisional conflict was significantly lower in both intervention groups but not in the control group. DA reduced decisional conflict from preintervention to postintervention (pre-post change) by 0.70 (SD = 0.56) points (on a 1-5 scale), compared to reductions of 0.51 (SD = 0.51) and 0.09 (SD = 0.44) for the DA + CC group and the control group, respectively. Satisfaction with the decision made was significantly higher at 2 weeks in the DA v. control group. Self-reported knowledge significantly improved in DA + CC compared to controls. CONCLUSION: Our decision aid lowered decisional conflict and improved patient satisfaction; adding coaching provided little additional benefit.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".