AB038. Assessing a novel approach to teaching ophthalmoscopy to medical student
Bibliographic record
Abstract
Background: To determine if practice using an online fundus photograph program results in a long-term increase in proficiency with the direct ophthalmoscope in medical students. Methods: This study was a prospective medical education trial. Students were enrolled to participate in an objective structured clinical examination (OSCE) using five patients with ocular findings. Students who matched a minimum of 6 discs 17 months prior to the study were assigned to the intervention group and were compared to students who did not participate in the exercise. Participants: 46 second-year medical students at Queen’s University: 15 in the intervention group, 31 in the control group. Students were evaluated using the Queen’s University Ophthalmoscopy OSCE Checklist (QUOOC). Students were asked to calculate the cup-to-disc ratio, comment on disc margins and if there was any macular pathology. Students participated in a summative OSCE as part of the curriculum in which all students attempted to match fundus photographs. Results: Students in the intervention group performed significantly better on the QUOOC with a mean score of 78.3% (+/−4.2%) compared to the control who had a mean score of 69.4% [+/−4.2% (P=0.007)]. The intervention group was significantly more accurate at matching optic nerve photographs with 100% (15/15) of the students correctly identifying the correct optic nerve on first attempt compared to 53.3% (16/30) in the control group (P=0.0014). Conclusions: The use of an online peer fundus photograph program leads to a long-term increase in examination technique, proficiency in ophthalmoscopy and accuracy at matching optic nerve photographs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".