Associations between perceptions of shared decision making and health among hysterectomy patients: A prospective observational study
Bibliographic record
Abstract
OBJECTIVE: To investigate patient and clinical factors that are associated with perceptions of shared decision making between hysterectomy patients and surgeons and to evaluate associations between shared decision making and postoperative health. METHODS: This study is based on a prospective cohort scheduled for hysterectomy for benign conditions in Vancouver, Canada. Validated patient-reported outcomes assessed shared decision making, pelvic health, depression, and pain. Regression analyses measured the association between perceptions of shared decision making with patient and clinical factors. Then, associations between shared decision making with postoperative pelvic health, pain and depression were evaluated using regression analysis and adjusted for patient and clinical factors. RESULTS: In this study, 308 participants completed preoperative measures, and a subset of 146 participants also completed the postoperative measures. More than 50% of participants reported less than optimal shared decision making scores. No significant associations were identified between patients' perceptions of shared decision making with patients' age, comorbidities, socioeconomic factors, indication for surgery, or preoperative depression and pain. Regression analyses found that higher/better self-reported shared decision making scores were associated with fewer postoperative pelvic organ symptoms (P = 0.01). CONCLUSION: Many patients' reporting lower than optimal scores on the shared decision making instrument highlight the opportunity to improve surgeon-patient communication in this surgical cohort. Strengthening shared decision making between surgeons and their patients may be associated with improved self-reported postoperative health.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".