Impact of Ophthalmic Knowledge Assessment Program Scores and Surgical Volume on Subspecialty Fellowship Application in Ophthalmology Residency: Retrospective Cohort Study
Bibliographic record
Abstract
Background: Ophthalmology residents take the Ophthalmic Knowledge Assessment Program (OKAP) exam annually, which provides percentile rank for multiple categories and the total score. In addition, ophthalmology residency training programs have multiple subspecialty rotations with defined minimum procedure requirements. However, residents' surgical volumes vary, with some residents exceeding their peers in specific subspecialty rotations. Objective: This study aims to identify if there is a difference in OKAP examination scores and surgical volume exposure during ophthalmology residency training between nonfellowship and fellowship applicants and among various subspecialties. Methods: A retrospective review of OKAP scores and surgical procedure numbers of graduating residents in an accredited academic ophthalmology residency program in the Midwest United States was conducted. Data were collected from 2012 to 2022. Results: A total of 31 residents were identified. Most residents decided to pursue fellowship training upon graduation (20/31, 65% residents), and the rest chose to practice comprehensive ophthalmology (11/31, 35% residents). A total of 18/31 residents had OKAP score reports available. The fellowship group outperformed the nonfellowship group in multiple subsections and the total exam (P=.04). Those pursuing fellowship training in glaucoma performed higher on the Glaucoma section (P=.004) and the total exam (P=.005). Residents pursuing cornea performed higher on nearly all subsections, including External Disease and Cornea (P=.02) and the total exam (P=.007). The majority of the surgical volume exposure was identical between fellowship and nonfellowship groups. Those who pursued glaucoma fellowship performed more glaucoma filtering and shunting procedures (P=.03). Residents going into pediatrics fellowship were primary surgeons in more strabismus cases (P=.01), assisted in fewer strabismus cases (P<.001), and had no difference in the total number of strabismus surgeries. Conclusions: In our program, residents pursuing fellowship training had higher OKAP scores on multiple sections and the total exam. There was no significant difference in the overall surgical volume averages between fellowship and nonfellowship groups, but few differences existed in subspecialty procedures among fellowship applicants. Larger multicenter studies are needed to clarify the relationship between OKAP scores and ophthalmology fellowship decisions nationwide.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".