What do urologists think patients need to know when starting on androgen deprivation therapy? The perspective from Canada versus countries with lower gross domestic product
Bibliographic record
Abstract
BACKGROUND: Androgen deprivation therapy (ADT) side effects are numerous and negatively impact prostate cancer patients' quality of life. There is considerable discrepancy though among Canadian urologists regarding what ADT side effects and side effect management strategies. Little is known about global differences in ADT patient education. METHODS: International respondents were recruited via online posting and at an international urology conference. Hypotheses suggest that economic and cultural differences influence patient education practices; therefore, international respondents were divided into 3 categories (high, medium, and low gross domestic product). RESULTS: No differences were found between responses from Canadian urologists and high GDP countries. Compared to responses from low GDP countries, Canadian urologists are more likely to endorse informing patients about: osteoporosis, loss of muscle mass, weight gain, fatigue/sleep disturbance, relationship changes, cognitive changes, and loss of body hair. Infertility was the only side effect more often disclosed by urologists in low GDP counties. Recommended management strategies for hot flashes are more likely to be pharmaceutical in Canada, and behavioral in low GDP countries. Management strategies for gynecomastia are emphasized more in low GDP countries. Physical exercise is endorsed consistently more often by Canadian urologists. CONCLUSIONS: ADT educational practices vary greatly between Canada and lower GDP countries. Factors that could contribute to differences include economics (e.g., ADT drug costs), differences in side effect presentation due to different ADT drugs used, racial differences in perceived side effect burden, disease status at ADT commencement, and cultural differences in patient-physician shared-decision making.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".