What Questions Do Patients with Curable Prostate Cancer Want Answered?
Bibliographic record
Abstract
PURPOSE: To determine the questions that recently diagnosed early-stage prostate cancer patients think should be addressed with patients like themselves. STUDY POPULATION: 56 patients diagnosed as having early-stage prostate cancer within the previous year. METHODS: Surveys distributed to the patients included 93 questions that might be considered important. Respondents judged the importance (essential/desired/no opinion/avoid) of addressing each question, and indicated why those "essential" or "desired" were important. RESULTS: 38 patients (68%) responded. Agreement on question importance, overall, was rather poor (mean 41.6%, kappa 0.17). There were, however, 20 questions that at least 67% of the respondents agreed were essential to address and 12 that they agreed were not essential. No question was relevant to the treatment decisions of more than 50% of respondents, but 91 questions were relevant to at least one respondent's decision. CONCLUSIONS: Although there was enough agreement to define a core set of questions that should be addressed with most patients, each of the remaining questions was also considered essential to some people. The core set, therefore, would not be adequate to satisfy any one patient's essential information needs. Whereas most questions would be needed to cover all patients' decision needs, only some are needed for any given patient. Such variability in information needs means that the subjective standard is the only viable legal standard for judging the adequacy of provision of information for the treatment decision.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".