A Patient Decision Aid for Men With Localized Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: There are multiple options for men diagnosed with localized prostate cancer. Patient decision aids (PtDAs) help empower individuals and reduce unwarranted practice variation, but few are used in clinical practice. OBJECTIVE: We compared 2 programs implementing PtDAs for men with localized prostate cancer. METHODS: This was a comparative case study. Case 1 was a hospital prostate pathway and case 2 was a provincial prostate pathway with 2 locations (2a, 2b). Nurses provided the men with PtDAs and answered questions. Data sources were as follows: (a) 2 years administrative data for men with localized prostate cancer, (b) clinicians survey and interviews, and (c) patients/spouses interviews. Analysis was within and across cases. RESULTS: The PtDA was used with 23% of men in case 1 (95% confidence interval, 19.8%-26.1%) and 98% of men in case 2a (95% confidence interval, 96.5%-99.8%). The pathway was not implemented in case 2b. Men given the PtDA had positive experiences. Many clinicians supported the use of PtDAs, some adapted their discussions with patients, and others did not support the use of PtDAs. To increase use in case 1, participants identified needing a Canadian PtDA available electronically and endorsed by all clinicians. In case 2b, the provincial prostate pathway needed to be implemented. CONCLUSIONS: There was variable uptake of the PtDAs between the cases. Men who received the PtDA had positive outcomes. Several strategies were identified to increase or sustain PtDA use. IMPLICATIONS FOR PRACTICE: Nurses have a key role in supporting men making decisions about prostate cancer treatment by providing PtDAs, answering questions, and advocating for men's preferences.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".