Patient‐focussed decision‐making in early‐stage prostate cancer: insights from a cognitively based decision aid
Bibliographic record
Abstract
PURPOSE: To study the cognitive processes of early-stage prostate cancer patients as they determined which treatment they preferred, using our cognitively based decision aid. METHOD: The aid was a one-to-one interview that included the structured presentation of information, listing exercises in which the patient identified attributes important to his decision, and trade-off exercises to help him weigh and integrate those attributes together. At various points of the interview, patients identified the attributes they felt were important to their decision, rated their treatment options and completed standardized assessments relating to their decision. In addition, patients participated in a follow-up interview at the time they made their actual treatment decision and again 3 months later. RESULTS: Sixty of 70 (86%) of the invited patients participated in the study. Participating patients identified a median of four important attributes (range 1-10); 36 different attributes were identified at some point in the interview by the group. During the interview, 78% of patients changed which attributes they considered important, and 72% changed their treatment ratings. Stability of treatment choice after the interview and lack of regret after the decision were each positively associated with increasing differentiation between treatment options over time. CONCLUSIONS: The decision process appears to be dynamic for the patients with great variability across patients in what is important to the decision. Increasing stability of choice and lack of regret appear to be related positively to increasing difference over time in how attractive the preferred option is over its closest competitor, rather than to the size of the difference at any one point in time.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".