Continued gender disparity in urology? Only time will tell
Bibliographic record
Abstract
eaders of CUAJ will likely be aware of the recent, provocative, Ontario-based study supporting previous literature highlighting gender-based disparities in medical practice.This cross-sectional, population-based study using administrative databases 1 documents contemporary inequity in income across surgical specialties, with marked differences in earnings between males and females.These imbalances were present after controlling for differences in hours worked or procedure duration.The authors contend that the opportunity to perform the most lucrative procedures is different between the sexes.Although the results for urological care were not statistically significant, the overall trend of these findings should stimulate a call for a fulsome analysis of drivers of gender-based disparities in our specialty.This issue of CUAJ expands on this topic, describing gender-related discrepancies in academic urology in Canada. 2 We asked Dr. Ashley Cox, program director at Dalhousie University and consulting editor of CUAJ, to comment on her thoughts and experiences.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".