Patients’ preferences for communicating a prostate cancer diagnosis and participating in medical decision‐making
Bibliographic record
Abstract
OBJECTIVES: To assess patients' preferences about how physicians' deliver news of a prostate cancer diagnosis, and patients' preferred participation in medical decision-making, with a secondary objective being to validate the Measure of Patients' Preferences (MPP) scale with these patients. PATIENTS AND METHODS: Eighty-seven men (mean age 62.4 years) referred to an ultrasound/radiology department for their first transrectal ultrasonography (TRUS)-guided biopsy completed the MPP and Control Preferences measures. Patients were asked to identify how they would like to be told about a potential prostate cancer diagnosis by their physician, and what role they would like in making treatment-related decisions with their physicians. RESULTS: Most patients wanted either an active (43%) or collaborative (47%) role in medical decision-making if the TRUS showed prostate cancer. Men rated content items (what and how much information is provided by their physician) as more important than supportive (emotional support during interaction) or facilitative (setting and context variables) items. Men who preferred a collaborative role in the patient-physician interaction wanted significantly (P = 0.04) more content (detailed information on available treatments and the effect of these treatments on their quality of life) than men who had a preference for either an active or passive role in medical decision-making. Demographic characteristics were not indicative of either preferred role in decision-making or communication preferences. The MPP was shown to be reliable. CONCLUSIONS: Men have expectations about how physicians disclose a diagnosis of prostate cancer and how they wish to participate in making treatment decisions. These results underline the importance of identifying patient preferences before embarking on treatment discussions, as the way 'bad news' is disclosed has previously been identified as a predictor of the outcome of the patient-physician interview.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".