Influential Factors Impacting Treatment Decision-making and Decision Regret in Patients with Localized or Locally Advanced Prostate Cancer: A Systematic Literature Review
Bibliographic record
Abstract
CONTEXT: Treatment decision-making (TDM) for patients with localized (LPC) or locally advanced (LAPC) prostate cancer is complex, and post-treatment decision regret (DR) is common. The factors driving TDM or predicting DR remain understudied. OBJECTIVE: Two systematic literature reviews were conducted to explore the factors associated with TDM and DR. EVIDENCE ACQUISITION: Three online databases, select congress proceedings, and gray literature were searched (September 2022). Publications on TDM and DR in LPC/LAPC were prioritized based on the following: 2012 onward, ≥100 patients, journal article, and quantitative data. The Preferred Reporting Items Reviews and Meta-analyses guidelines were followed. Influential factors were those with p < 0.05; for TDM, factors described as "a decision driver", "associated", "influential", or "significant" were also included. The key factors were determined by number of studies, consistency of evidence, and study quality. EVIDENCE SYNTHESIS: Seventy-five publications (68 studies) reported TDM. Patient participation in TDM was reported in 34 publications; overall, patients preferred an active/shared role. Of 39 influential TDM factors, age, ethnicity, external factors (physician recommendation most common), and treatment characteristics/toxicity were key. Forty-nine publications reported DR. The proportion of patients experiencing DR varied by treatment type: 7-43% (active surveillance), 12-57% (radical prostatectomy), 1-49% (radiotherapy), 28-49% (androgen-deprivation therapy), and 21-47% (combination therapy). Of 42 significant DR factors, treatment toxicity (sexual/urinary/bowel dysfunction), patient role in TDM, and treatment type were key. CONCLUSIONS: The key factors impacting TDM were physician recommendation, age, ethnicity, and treatment characteristics. Treatment toxicity and TDM approach were the key factors influencing DR. To help patients navigate factors influencing TDM and to limit DR, a shared, consensual TDM approach between patients, caregivers, and physicians is needed. PATIENT SUMMARY: We looked at factors influencing treatment decision-making (TDM) and decision regret (DR) in patients with localized or locally advanced prostate cancer. The key factors influencing TDM were doctor's recommendation, patient age/ethnicity, and treatment side effects. A shared, consensual TDM approach between patients and doctors was found to limit DR.
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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 | 0.016 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".