Incidence and predictors of delays in commencing fellowship training in urology
Bibliographic record
Abstract
INTRODUCTION: The completion of residency and start of fellowship training marks a critical transition for urologists in the pursuit of subspeciality training. Most graduating urology residents are under contract until June 30, and most fellowships are scheduled to begin on July 1. There has been no investigation into the practical implications of fellowship delays in urology from a trainee perspective. Our research study aimed to investigate the incidence and predictors of delays in fellowship starts. METHODS: . A total of 250 endourology (EU) fellows and 90 pediatric urology (PU) fellows were contacted. RESULTS: A total of 26.0% and 14.3% of EU and PU fellows, respectively, experienced a delay in their training, despite many leaving their residency positions early (33.8% vs. 44.9%, p=0.2097); 11.7% and 8.2% of EU and PU fellows, respectively, experienced delays they reported to be "very stressful" and 9.1% and 4.1%, respectively, found them "somewhat stressful." Delays of 2-4 weeks were experienced by 5.2% and 6.1%, 4-6-week delays by 7.8% and 4.1%, and delays >6 weeks by 2.6% and 0% of EU and PU fellows, respectively (p=0.0007). CONCLUSIONS: Delays in fellowship training do occur at a notable rate, despite nearly half of urology fellows leaving their residency training positions early, with unclear impacts on patient care and resident colleague well-being. This research highlights the importance of fellowship programs considering delaying fellowship starts to mid-July or August, with support of the prior fellow cohorts.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".