Information that affects patients' treatment choices for early stage prostate cancer: a review.
Bibliographic record
Abstract
INTRODUCTION: We conducted a systematic review of primary evidence to clarify what information influences treatment selection by patients with early stage prostate cancer. MATERIALS AND METHODS: We conducted a systematic review of the Web of Knowledge, using the ALL DATABASES option. Papers were then triaged out on the basis of the title and/or abstract, leaving 120 papers. Reviewing the full papers resulted in a final corpus of 21 papers. RESULTS: The data suggest that patients typically balance potential benefits against potential side effects but in a complex way with large variation across patients. For some patients, potential benefits relate to chances of survival but, for others, relate to control over cancer spread. The most common potential harm is effect on bladder functioning but even that is not a concern of all patients. Similarly, potential impact on bowel and on sexual functioning affects some patients' decisions but not others. Patient decisions are also affected by information not typically identified as affecting this decision. These include aspects of treatment and decision processes, competencies, and others' opinions, again, with wide variation across patients. The patient's view of which information items affect his decision may also change over time, consistent with a dynamic decision-making process. CONCLUSIONS: Decision support interventions are needed to optimally tailor information for decision-making to the individual patient, and should be designed to accommodate the illustrated variation in patients' needs.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".