Androgen deprivation therapy for prostate cancer: Prescribing behaviors and preferences among urologists
Bibliographic record
Abstract
INTRODUCTION: Several androgen deprivation therapy (ADT) medications are available for treating advanced prostate cancer with roughly equivalent oncological efficacy and tolerability. We investigated the proportion of physicians who predominantly prescribe one type of ADT drug ("mono-prescriber") and assessed characteristics associated with prescription behavior. METHODS: Ontario men aged ≥65 years who were diagnosed with advanced prostate cancer (1997-2017) and initiated ADT thereafter for ≥3 consecutive months were identified using population-level administrative data. Their first prescription for injectable ADT was linked to a physician, and urologists with ≥10 prescriptions over the study period were included in the analysis (n=282). Urologists were classified as high mono-prescribers if ≥80% of their prescriptions were for one drug type. Multivariable logistic regression was used to examine the association of physician characteristics with the odds of being a high mono-prescriber. RESULTS: Overall, 67 (23.8%) of urologists were classified as high mono-prescribers but the frequency varied across health planning regions. The most commonly prescribed drugs and those used by mono-prescribers were goserelin (41.8% and 56.7%) and leuprolide (44.3% and 43.3%), respectively. In multivariable analysis, the odds of a physician being a high mono-prescriber were higher with more years in practice (odds ratio [OR] 1.06/year, 95% confidence interval [CI] 1.03-1.09, p<0.0001) and lower for higher patient volume (OR 0.33 for above vs. below median, 95% CI 0.17-0.63, p=0.0008). CONCLUSIONS: Overall, one in four urologists were classified as high mono-prescribers. Mono-prescribers had more years in practice and smaller volume practices, potentially suggesting habitual prescription behavior and/or the effect of external pressures.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".