Medical student competence in ophthalmology assessed using the Objective Standardized Clinical Examination
Bibliographic record
Abstract
Purpose: To assess pre-clerkship and clerkship medical student performance in an ophthalmology Objective Standardized Clinical Examination (OSCE) station. Methods: One hundred pre-clerkship medical students and 98 clerkship medical students were included in this study. The OSCE station consisted of a common ocular complaint - blurry vision with decreased visual acuity - and students were asked to take an appropriate history, provide two or three differential diagnoses to explain the symptoms, and perform a basic ophthalmic examination. Results: Generally, clerks performed better than pre-clerks in the history taking (P < 0.01) and ophthalmic examination (P < 0.05) sections, with few specific exceptions. In the history-taking section, more pre-clerkship students asked about patient age and past medical history (P < 0.00001) and for the ophthalmic examination, more pre-clerkship students performed the anterior segment examination (P < 0.01). Interestingly, more pre-clerkship students were also able to provide two or three differential diagnoses (P < 0.05), specifically diabetic retinopathy (P < 0.00001) and hypertensive retinopathy (P < 0.00001). Conclusion: The performance of both groups was generally satisfactory; however, many students in both groups had scores that were unsatisfactory. Notably, pre-clerks also outperformed clerks in certain areas, which emphasizes the importance of revisiting ophthalmology content through clerkship. Awareness of such knowledge can allow medical educators to incorporate focused programs into the curriculum.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".