Implementation of a patient decision aid for men with localized prostate cancer: evaluation of patient outcomes and practice variation
Bibliographic record
Abstract
BACKGROUND: Men with localized prostate cancer often have unrealistic expectations. Practitioners are poor judges of men's preferences, contributing to preference misdiagnosis and unwarranted practice variation. Patient decision aids (PtDAs) can support men with decisions about localized prostate cancer. This is a comparative case study of two strategies for implementing PtDAs in clinical pathways for men with localized prostate cancer, evaluating (a) PtDA use; (b) impact on men, practitioners, and health system outcomes; and (c) factors influencing sustained use. METHODS/DESIGN: Guided by the Knowledge to Action Framework, this comparative case study will be conducted using administrative data, interviews, and surveys. Cases will be bound by geographic location (one hospital in Ontario; province of Saskatchewan) and time. Eligible participants will be all men newly diagnosed with localized prostate cancer, with outcomes assessed using administrative data and interviews. Nurses, urologists, radiation oncologists, and managers will be surveyed and a smaller sample interviewed. Cases will be established for each setting with findings compared across cases. Changes in the proportions of men given the PtDA over 2 years will be determined from administrative data. Factors associated with receiving the PtDA will be explored using multivariable logistic regression analysis. To assess the impact of the PtDA, outcomes will be described using mean and standard deviation (men's decisional conflict) and frequency and proportions (practitioners consulted, uptake of treatment). To estimate the effect of the PtDA on these outcomes, adjusted mean differences and odds ratios will be calculated using exploratory multivariable general linear regression and binary or multinomial logistic regression. Factors influencing sustained PtDA use will be assessed using descriptive analysis of survey findings and thematic analysis of interview transcripts. DISCUSSION: Determining how to embed PtDAs effectively within clinical pathways for men with localized prostate cancer is essential. PtDAs have the potential to strengthen men's active role in making prostate cancer decisions, enhance uptake of shared decision-making by practitioners, and reduce practice variation. Our team of researchers and knowledge users will use findings to improve current PtDA use and consider scaling-up implementation.
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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 | 0.040 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".