Descriptive Analysis of Vitreoretinal Surgery Fellowship Program Directors
Bibliographic record
Abstract
Purpose: To evaluate the demographic, educational, and scholarly characteristics of Association of University Professors of Ophthalmology–accredited vitreoretinal surgery fellowship program directors in the United States and Canada. Methods: Demographic, educational, and scholarly profiles of identified program directors were collated from online public resources. Characteristics were compared by sex, program size, ranking, and affiliation. Results: Eighty-one program directors (mean age [±SD] 54.7 ± 11.0 years) from 78 fellowship programs were identified. The minority were women (14.8%), who were on average 6 years younger than their male counterparts ( P = .07). The majority of program directors had an academic affiliation (90.1%), most commonly professor (54.8%). The mean h-index, 5-year h-index, and m-quotient were 20.9 ± 14.9, 5.9 ± 4.4, and 0.82 ± 0.42, respectively. Compared with their counterparts, program directors of both “top 10” and large programs published more manuscripts ( P < .05), accrued more citations ( P < .05), and had a higher h-index ( P < .05). Fellowship programs with female program directors had a significantly larger proportion of female retina faculty ( P = .002). Conclusions: The backgrounds of vitreoretinal surgery program directors are diverse. However, women remain underrepresented in this position, highlighting an area with the potential for greater equity in ophthalmology.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".