Informed Choice Assistance for Women Making Uterine Fibroid Treatment Decisions: A Practical Clinical Trial
Bibliographic record
Abstract
BACKGROUND: There is limited evidence about how to ensure that patients are helped to make informed medical care decisions. OBJECTIVE: To test a decision support intervention for uterine fibroid treatments. DESIGN AND SETTING: Practical clinical trial to test informed choice assistance in 4 randomly assigned gynecology clinics compared to 5 others providing a pamphlet. PATIENTS: Three hundred women facing a treatment decision for fibroids over a 13-month period. INTERVENTION: Mailed DVD and brochure about fibroid treatments plus the Ottawa decision guide and an offer of counseling soon after an index visit. MEASUREMENTS: Mailed survey 6 to 8 weeks later asking about knowledge, preferences, and satisfaction with decision support. RESULTS: In total, 244 surveys were completed for an adjusted response rate of 85.4%. On a 5-point scale, intervention subjects reported more treatment options being mentioned (3.0 v. 2.4), had a higher knowledge score (3.3 v. 2.8), and were more likely to report being adequately informed (4.4 v. 4.0), and their decision was both more satisfactory (4.3 v. 4.0) and more consistent with their personal values (4.5 v. 4.2). Neither knowledge nor use of the intervention was associated with greater concordance between preferences and decisions. LIMITATIONS: Implementation of intervention may not have been well timed to the decision for some patients, limiting their use of the materials and counseling. CONCLUSION: It is difficult to integrate structured decision support consistently into practice. Decision support for benign uterine conditions showed effects on knowledge and satisfaction but not on concordance.
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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 | 0.013 | 0.023 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".