Developing a Decision Aid to Support Informed Choices for Newly Diagnosed Patients With Localized Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Decision aids (DAs) have been developed in several health disciplines to support decision making informed by evidence, such as the benefits and risks of different treatment options. Decision aids can improve the decision-making process by reducing decisional conflict and helping patients to participate in decision making. OBJECTIVE: The aim of this study was to design and develop a DA for treatment decision making in localized prostate cancer in Spain with regard to surgery, radiotherapy, or watchful waiting. INTERVENTIONS/METHODS: We developed a DA based on the principles of the International Patient Decision Aid Standards Collaboration and according to the Ottawa Decision Support Framework. The structural development process involved DA developers, expert feedback, use of the Delphi method, and patient feedback. We conducted a pilot test on 34 men with localized prostate cancer. RESULTS: The DA is a structured booklet. According to the International Patient Decision Aid Standards checklist, the DA scored 22 of 27 points (81.48%). The development process section scored 22 of 24 points (91.6%), and the effectiveness of the decision-making process section scored 6 of 6 (100%). The clinical pilot test yielded positive feedback regarding the design, images, understandability, usability, explanations, and amount of information in the DA. CONCLUSIONS: We developed a Spanish DA with a strong quality score to help patients make an informed choice regarding their prostate cancer treatment. Future research will assess the impact of the DA and its association with improved decision making. IMPLICATIONS FOR PRACTICE: This tool provides information about the risks and benefits of different treatment options and helps patients to understand the importance of their own values for informing treatment choices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.007 | 0.002 |
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".