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PD32-10 SETTING THE STANDARDS

2020· article· en· W3023967850 on OpenAlexaboutno aff
Timothy Han, Lydia Glick, Joon Yau Leong, Seth Teplitsky, Rodrigo Noorani, Hanan Goldberg, Zachary Klaassen, C. Edward Wallis, James Ryan Mark, Mark Mann, Edouard J. Trabulsi, Costas D. Lallas, Leonard G. Gomella, Thenappan Chandrasekar

Bibliographic record

VenueThe Journal of Urology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAccreditationLogistic regressionGraduate medical educationDescriptive statisticsIndex (typography)Family medicineMedical educationProductivityDemographyStatisticsInternal medicine

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.087
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.120
Threshold uncertainty score0.401

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.087
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0040.004
Scholarly communication0.0140.005
Open science0.0060.012
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.1200.086

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.

Opus teacher head0.026
GPT teacher head0.302
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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