Personal prostate-specific antigen screening and treatment choices for localized prostate cancer among expert physicians
Bibliographic record
Abstract
INTRODUCTION: We aimed to determine the personal practices of urologists, radiation oncologists, and medical oncologists regarding prostate cancer screening and treatment using the physician surrogate method, which seeks to identify acceptable healthcare interventions by ascertaining interventions physicians select for themselves. METHODS: A hierarchical, contingent survey was developed through a consensus involving urologists, medical oncologists, and radiation oncologists. It was piloted at the University of Toronto and then circulated to urologists, radiation oncologists, and medical oncologists through professional medical societies in the U.S., Canada, Central and South America, Australia, and New Zealand. The primary outcome was physicians' personal choices regarding prostate-specific antigen (PSA) screening and the secondary outcome was treatment selection among those diagnosed with prostate cancer. RESULTS: A total of 869 respondents provided consent and completed the survey. Of these, there were 719 urologists, 89 radiation oncologists, nine medical oncologists, and 53 undisclosed specialists. Most (784 of 869 respondents; 90%) endorsed past or future screening for themselves (among male physicians) or for relatives (among female physicians). Among urologists and radiation oncologists making prostate cancer treatment decisions, there was a significant correlation between physician specialty and the treatment selected (Phi coefficient=0.61; p=0.001). CONCLUSIONS: Physicians who routinely treat prostate cancer are likely to undertake prostate cancer screening themselves or recommend it for immediate family members. Treatment choice is influenced by the well-recognized specialty bias.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".