Patient-Centered Approach to Develop the Patient’s Preferences for Prostate Cancer Care (PreProCare) Tool
Bibliographic record
Abstract
Objectives. To describe the development of our Patient Preferences for Prostate Cancer Care (PreProCare) tool to aid patient-centered treatment decision among localized prostate cancer patients. Methods. We incorporated patient and provider experiences to develop a patient preference elicitation tool using adaptive conjoint analysis. Our patient-centered approach used systematic literature review, semistructured patient interviews, and provider focus groups to determine the treatment attributes most important for decision making. The resulting computer-based PreProCare tool was pilot tested in a clinical setting. Results. A systematic review of 56 articles published between 1995 and 2015 yielded survival, cancer recurrence, side effects, and complications as attributes of treatment options. We conducted one-on-one interviews with 50 prostate cancer survivors and 5 focus groups of providers. Patients reported anxiety, depression, treatment specifics, and caregiver burden as important for decision making. Providers identified clinical characteristics as important attribute. Input from stakeholders’ advisory group, physicians, and researchers helped finalize 15 attributes for our PreProCare preference assessment tool. Conclusion. The PreProCare tool was developed using a patient-centered approach and may be a feasible and acceptable preference clarification intervention for localized prostate cancer patients. The PreProCare tool may translate into higher participant engagement and self-efficacy, consistent with patients’ personal values.
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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 | 0.046 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".