Do patients benefit from participating in medical decision making? Longitudinal follow-up of women with breast cancer
Bibliographic record
Abstract
This study sought to examine the relationships between decisional role (preferred and assumed) at time of surgical treatment (baseline), congruence between assumed role at baseline and preferred role 3 years later (follow-up), and quality of life at follow-up. Two hundred and five women diagnosed with breast cancer completed the decisional role preference scale at baseline and follow-up, and the EORTC QLQ-C30 at follow-up. A statistically significant number of women had decisional role regret, with most of these women preferring greater involvement in treatment planning than was afforded them. Women who indicated at baseline that they were actively involved in choosing their surgical treatment had significantly higher overall quality of life at follow-up than women who indicated passive involvement. These actively involved women had significantly higher physical and social functioning and significantly less fatigue than women who assumed a passive role. Quality of life was significantly related to reports of experienced involvement in treatment decision making, but not to reports of preferred involvement, or congruence between preferred and experienced involvement.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".