<p>Shared decision making for men facing prostate cancer treatment: a systematic review of randomized controlled trials</p>
Bibliographic record
Abstract
Aims: To synthesize the empirical evidence on the effectiveness of shared decision making (SDM) compared to usual care for prostate cancer (PC) treatment. Methods and results: A systematic review of academic (MEDLINE, EMBASE, Cochrane Library, CINHAL, PsychINFO, and Scopus) and grey (clinicaltrials.gov, WHO trial search, meta-Register ISRCTN, Google Scholar, opengrey, and ohri.ca) literature, also identified from contacting authors and hand-searching bibliographies. We included randomized controlled trials (RCTs): 1) comparing SDM to usual care for decisions about PC treatment, 2) conducted in primary or specialized care, 3) fulfilling the key SDM features, and 4) reporting quantitative outcome data. Four RCTs from Canada (n=3) and the USA were included and comprised 1,065 randomized men, most (89.8%) of whom were in PC stage T1-T2. The studies reported 24 outcome measures. In 62.5% study estimates, SDM was similar to usual care at improving patient satisfaction and mood, and at reducing decisional conflict and decisional regret. In 37.5% study estimates, SDM significantly improved knowledge, perception of being informed and patient-perceived quality of life (QoL) at four weeks. There was a dearth of outcome data, particularly on the adherence to treatment and on patient-important and clinically relevant health outcomes such as symptoms and mortality. Conclusion: SDM may positively influence men’s knowledge and may have a positive but short-term effect on patient-perceived QoL. The (long-term) effects of SDM on patient-related outcomes for decisions about PC treatment are unclear. Future research needs consensus about the interventions and outcomes needed to evaluate SDM and should address the absence of evidence on health outcomes. Keywords: systematic review, shared decision making, prostate cancer, treatment, controlled clinical trials, urology
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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 | 0.033 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".