The Ophthalmology Surgical Competency Assessment Rubric for penetrating keratoplasty
Bibliographic record
Abstract
PURPOSE: Irreversible corneal blindness disproportionately affects low- and middle-income countries where there remains a critical demand for surgeons skilled in corneal transplantation. Penetrating keratoplasty (PK) is a critical skill for ophthalmologists, especially as it remains the most widely performed type of corneal transplant in these parts of the world. Tools to teach and assess trainees in corneal transplantation are needed to help standardize training internationally. Here, we present an Ophthalmology Surgical Competency Assessment Rubric (OSCAR) aimed at assessing the competence and progress of residents in PK. METHODS: A team of cornea specialists developed a rubric for PK using previously published OSCARs as a template. The draft included 11 key steps of PK and six global indices, with a grading scale based on a modified Dreyfus model. The rubric was then reviewed for face and content validity by a panel of eight international experts using an iterative feedback process. The draft underwent multiple cycles of feedback and revisions until a consensus was reached among the authors and the international expert panel. RESULTS: The rubric contains 11 essential steps and six global indices with descriptions of expected functioning at four levels of competency (novice, beginner, advanced beginner, and competent). CONCLUSIONS: This OSCAR for PK contributes to the global standardization of ophthalmology training, helping to meet the increasing demand for corneal transplant services with high-quality care for all.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".