Fellowship Selection Criteria in Ophthalmic Plastic and Reconstructive Surgery
Bibliographic record
Abstract
INTRODUCTION: Competition for subspecialty fellowship positions in ophthalmology continues to grow, and there is increasing interest regarding the factors considered important in fellowship selection. While a previous report evaluated the characteristics and criteria used by ophthalmology subspecialty program directors to select fellows in retina, cornea/external disease, and glaucoma fellowship programs, to the authors' knowledge no such study has evaluated Ophthalmic Plastic and Reconstructive Surgery (OPRS) fellowships. METHODS: The authors surveyed the program directors of all American Society of Ophthalmic Plastic and Reconstructive Surgery (ASOPRS)-sponsored fellowships in the United States and Canada. The survey contained 16 criteria related to the selection of fellows. A Likert scale ranging from 1 (not important) to 9 (very important) was used for prioritizing the criteria. Opportunity was afforded for comment on other measures, and program directors were also asked to select their most important factor used for fellow selection. RESULTS: The return rate of the completed surveys was 35 of 48 (73%). The 3 criteria with the highest mean Likert scale scores were the interview process (8.7), the ability to work and communicate with others (8.5), and letters of recommendation from subspecialty faculty (7.8). Likewise, the criterion selected as the single most important by respondents was the interview (58%), the ability to work and communicate with others (15%), and letters of recommendation from subspecialty faculty (15%). CONCLUSIONS: The authors' findings demonstrate that OPRS program directors place greater emphasis on qualities assessed during the interview, letters of recommendation from same specialty faculty, and the ability of the applicant to work and communicate with others. While not identical, our findings were similar to those noted for other ophthalmology subspecialties. The results support the suggestion that residents interested in fellowship training may benefit from faculty mentors in their area of interest early in their training. With the high interest in OPRS and other ophthalmology subspecialty fellowship training, the authors hope that this report will be useful to applicants, residency programs, and fellowship directors.
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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 | 0.019 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".