Manuscript Publication by Urology Residents and Predictive Factors
Bibliographic record
Abstract
PURPOSE: Many academic institutions have set expectations for peer reviewed publications, yet there is no objective guideline to gauge the performance of a urology resident or program. We quantified and determined predictive factors for resident manuscript production. MATERIALS AND METHODS: Electronic surveys were sent to 255 chief residents and recent graduates of 83 accredited urological training programs in the United States and Canada. Survey questions pertained to manuscript submission and acceptance before and during residency, months of research incorporated into residency, PhD degree status and the pursuit of fellowship training. RESULTS: Surveys were completed by 127 residents from 83 programs. The median number of manuscripts submitted and accepted during residency was 3 (range 0 to 32) and 2 (range 0 to 25), respectively. Months of protected research time and the number of publications before residency were significantly predictive of the number of manuscripts submitted during residency (p <0.001 and p <0.001, respectively). The number of manuscripts submitted during residency was significantly associated with entering fellowship training (p <0.05). CONCLUSIONS: Manuscript preparation and publication are important aspects of the training process at a number of urological surgery residency programs. While the majority of residents are not involved in publication before residency, most submit and publish at least 1 manuscript as first author in a peer reviewed journal during residency. The number of prior publications and months of allotted research time are significantly predictive of resident manuscript productivity. In turn, manuscript submission is indicative of the decision to pursue fellowship training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".