Experiences and Outcomes of the Society of Genitourinary Reconstructive Surgeons Fellowship Training: Growth in Fellowships Mirrors the Evolution of the Discipline
Bibliographic record
Abstract
OBJECTIVE: To describe experiences and outcomes of the Society of Genitourinary Reconstructive Surgeons (GURS) fellowship match. In 2012, GURS developed a centralized fellowship match which has grown from 13 to 30 programs. METHODS: GURS match statistics and case logs were reviewed from 2013-2024. Additionally, a 37-question survey evaluating satisfaction, procedural competency, fellowship experience, and employment opportunities were sent to graduates. Linear regression was performed to examine trends over time. RESULTS: Over the study period, program match success remained stable (94.7%; P = .50) while applicant success (63.3%) increased over time (P = .04). North American and female applicants experienced higher match success (72.8% and 73.6%) compared to their international (35.5%; P <.0001) and male counterparts (60.0%; P = .02). On case log analysis, mean surgical volumes per year increased in urethral reconstruction (mean=88.1; P = .02), male sexual health (n = 32.7; P = .03), genital reconstruction (mean=16.4; P <.01) and abdominal reconstruction (mean=24.5; P = .03). Male incontinence surgeries remained stable (mean=30.5; P = .21) while female reconstruction declined (mean=23.2; P = .01). With a survey response rate of 54.5% (97/178), training satisfaction was 95.9% which did not differ by gender (P = .54) or year of training (P = .22). Around 97.9% felt competent to enter unsupervised reconstructive practice, 94.8% reported an understanding of the relevant literature and 96.9% were satisfied with their job as a reconstructive urologist. Around 49.5% identified a different case mix in practice compared to fellowship, most commonly related to abdominal (44.9%) or genital reconstruction (16.3%). CONCLUSION: GURS fellowships have grown organically over the last decade and mirror the growth and evolution of the discipline with sustained high levels of graduate satisfaction, surgical experience, competence, scholarly inquiry and employment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".