PD32-10 SETTING THE STANDARDS
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: Research productivity amongst academic urologists is strongly encouraged, but little data is available on productivity metrics within the field of urology. We provide the first comprehensive survey of research productivity amongst academic urologists in the United States and Canada. METHODS: Using the Accreditation Council for Graduate Medical Education (ACGME), the Canadian Resident Matching Service (CaRMS), and individual program websites, all active accredited urology faculty were identified. For each individual, we collected data on AUA section, title, gender, fellowship training, Scopus H-index and citations. Comprehensive searches were completed during March-May 2019. Descriptive statistics for demographic comparisons were performed using analysis of variance (ANOVA) for continuous variables and chi-square test for categorical variables. Multivariable logistic regressions were used to identify predictors of H-index greater than the median. RESULTS: 2214 academic urology faculty (2015 USA, 199 Canada) were identified. Median and mean H-indices for the entire cohort of physicians were 11 and 16.1, respectively (Figure 1). Table 1 highlights the median H-index stratified by academic title, AUA section, gender, and fellowship training. On multivariable analysis, physicians in the North Central and Western Sections (vs. Mid-Atlantic), who were fellowship-trained (vs. no fellowship training), and of higher academic rank (Professor and Associate Professor vs. clinical instructor) were more likely to have H-index values greater than the median. Additionally, female physicians (vs. male) were more likely to have H-index values less than the median. CONCLUSIONS: This study represents the first comprehensive assessment of research productivity metrics amongst academic urologists. These represent key benchmarks for trainees considering careers in academics and for practicing physicians gauging their own productivity in relation to their peers.Source of Funding: N/A
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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 | 0.035 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.120 | 0.086 |
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".