MP54-11 TRENDS IN UROLOGY SUBSPECIALTY MATCHING: A RETROSPECTIVE ANALYSIS OF AUA FELLOWSHIP APPLICANTS
Bibliographic record
Abstract
You have accessJournal of UrologyDiversity, Equity & Inclusion: Increasing Representation in Urology (MP54)1 May 2024MP54-11 TRENDS IN UROLOGY SUBSPECIALTY MATCHING: A RETROSPECTIVE ANALYSIS OF AUA FELLOWSHIP APPLICANTS Kamil Malshy, Keith Rourke, Borivoj Golijanin, Sari Khaleel, Simone Thavaseelan, Gyan Pareek, and Dragan Golijanin Kamil MalshyKamil Malshy , Keith RourkeKeith Rourke , Borivoj GolijaninBorivoj Golijanin , Sari KhaleelSari Khaleel , Simone ThavaseelanSimone Thavaseelan , Gyan PareekGyan Pareek , and Dragan GolijaninDragan Golijanin View All Author Informationhttps://doi.org/10.1097/01.JU.0001008944.36895.9d.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The American Urological Association's (AUA) accredited fellowships are competitive, attracting a global pool of applicants every year. This study aims to gain insight into the trends in applicant matching in various urological subspecialties. METHODS: We retrospectively reviewed match statistics of all 5 fellowship programs accredited by the AUA from 2010 - 2024. Societies included the Endourological Society (EUS), Society for Urological Oncology (SUO), American Society of Andrology (AMA), Society of Genitourinary Reconstructive Surgeons (GURS) and Society of Pediatric Urology (SPU). Applicants were classified according to 2 categories: postgraduate training (US/Canada (US/Ca) vs foreign graduates (FGs)) and gender (M vs F). Based on the above-mentioned applicant classifications, we provided applicant numbers, interviews offered, and overall match rates. RESULTS: Across the 5 programs, 2429 applicants applied. 1998 males (82.3%), 399 females (16.4%) and 32 undisclosed (1.3%). There were 1486 US/Ca graduates (60.8%) and 953 FGs (39.2%). The average number of vacancies listed by the EUS, ASA, SUO, GURS and SPU were 42.1 (±13.9), 13 (±4), 50.3 (±2.7), 20.9 (±4.6) and 26.1 (±1.6) respectively. 1471 (60.6%) applicants were matched with a program, compared to 958 (39.4%) unmatched, p<0.001. The probability of US/Ca graduates matching is significantly higher with 1246/1486 (83.8%) than that of FGs 222/953 (23.3%), p<0.001. In GURS, FGs have the best match rate of 47/118 (33.8%), and SPU the lowest 1/14 (7%). Female applicants have a significantly higher chance of matching 324/399 (81.2%) than male applicants 1139/1998 (57%), p<0.001. US/Ca-to-FGs ratios and the male-to-female ratio were stable throughout the match years (Figure 1). Table 1 demonstrates detailed results. CONCLUSIONS: US/Ca graduates and female applicants have higher matching rates across the years and subspecialties. Sustained initiatives to foster diversity and the potential expansion of urology subspecialty positions are essential. Acknowledgment: Ms. Sandra Howard (AUA). Download PPT Source of Funding: None © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 211Issue 5SMay 2024Page: e879 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information Kamil Malshy More articles by this author Keith Rourke More articles by this author Borivoj Golijanin More articles by this author Sari Khaleel More articles by this author Simone Thavaseelan More articles by this author Gyan Pareek More articles by this author Dragan Golijanin More articles by this author Expand All Advertisement PDF downloadLoading ...
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".