Evaluating the cost-effectiveness of the Prostate Cancer Patient Empowerment Program
Bibliographic record
Abstract
INTRODUCTION: This study aimed to evaluate the cost-effectiveness of the Prostate Cancer Patient Empowerment Program (PC-PEP), a six-month comprehensive intervention designed to enhance psychological well-being and reduce healthcare expenditures among prostate cancer patients. METHODS: In a crossover randomized clinical trial of 128 men aged 50-82 years scheduled for curative prostate cancer surgery or radiotherapy (± hormone treatment), 66 men received the PC-PEP intervention immediately, while 62 were randomized to a waitlist control arm and received standard care for six months before receiving PC-PEP. The intervention included daily activities targeting physical fitness, pelvic floor training, stress management, intimacy, social support, and dietary guidance. Cost-effectiveness was assessed from a healthcare payer perspective using billing data from Nova Scotia's Medical Services Insurance (MSI) and self-reported outcomes. Incremental cost-effectiveness ratios (ICERs) and cost-effectiveness acceptability curves (CEACs) were calculated using bootstrapped samples. Psychological distress was assessed with the Kessler Psychological Distress Scale (K10), while quality-adjusted life years (QALYs) were estimated from SF-6D utility scores. RESULTS: PC-PEP resulted in cost savings of $411.53 CAD per patient at six months, with a 30% reduction in clinically significant psychological distress and a QALY gain of 0.013. At 12 months, savings increased to $660.89 CAD per patient, preventing 31% of distress cases and yielding a QALY gain of 0.034. These outcomes demonstrate that PC-PEP is a dominant intervention, achieving both improved clinical outcomes and reduced healthcare expenditures. CONCLUSIONS: PC-PEP is a dominant, cost-effective strategy that significantly improves psychological well-being while lowering healthcare costs. Early implementation following prostate cancer diagnosis is strongly recommended to maximize both clinical and economic benefits.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".