Decision aids for localized prostate cancer treatment choice: Systematic review and meta‐analysis
Bibliographic record
Abstract
Patients who are diagnosed with localized prostate cancer need to make critical treatment decisions that are sensitive to their values and preferences. The role of decision aids in facilitating these decisions is unknown. The authors conducted a systematic review of randomized trials of decision aids for localized prostate cancer. Teams of 2 reviewers independently identified, selected, and abstracted data from 14 eligible trials (n = 3377 men), of which 10 were conducted in North America. Of these, 11 trials compared decision aids with usual care, and 3 trials compared decision aids with other decision aids. Two trials suggested a modest positive impact on decisional regret. Results across studies varied widely for decisional conflict (4 studies), satisfaction with decision (2 studies), and knowledge (2 studies). No impact on treatment choices was observed (6 studies). In conclusion, scant evidence at high risk of bias suggests the variable impact of existing decision aids on a limited set of decisional processes and outcomes. Because current decision aids provide information but do not directly facilitate shared decision making, subsequent efforts would benefit from user-centered design of decision aids that promote shared decision making.
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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 | 0.018 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.020 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".