Patient Preference and the Impact of Decision-Making Aids on Prostate Cancer Treatment Choices and Post-Intervention Regret
Bibliographic record
Abstract
The number of prostate cancer survivors is rapidly growing in the Western world. As a result of better oncologic outcomes, more patients are living longer with the adverse effects of treatment, which can be both functional and psychological. Clinicians, in an era of shared decision-making, must not only cure the cancer, but also ensure that, after treatment, their patients experience the best quality of life and minimal post-treatment decisional regret. To participate in the decision-making process, men and their involved partners and family need to fully understand the relative benefits and harms of prostate cancer treatments.Patient preference studies indicate that men with prostate cancer are not well informed. Decision-making aids are a positive treatment adjunct both to convey information and to allow patients to explore their own beliefs and values during the decision-making process. The evidence suggests that decision-making aids better prepare patients for involvement in treatment decisions, but further studies are required to investigate the relationship between the use of decision-making aids and post-treatment decisional regret in prostate cancer.
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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 | 0.012 | 0.124 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".