Evaluation of an Ophthalmoscopy Simulator to Teach Funduscopy Skills to Pediatric Residents
Bibliographic record
Abstract
BACKGROUND: Medical school and residency training in ophthalmoscopic evaluation is limited, reducing diagnostic accuracy. We sought to evaluate the efficacy of self-study using an ophthalmoscopy simulator to improve the technical motor skills involved in direct funduscopy in postgraduate pediatric residents. METHODS: In this randomized-controlled study, 17 pediatric residents (postgraduate years 1-3) were randomized to control (n=8) or intervention (n=9) groups. Participants were asked to correctly identify the funduscopic findings presented to them on an ophthalmoscopy simulator after being trained on its use. Each participant was asked to review 20 images of the fundus, and then record their multiple-choice response on a scantron sheet listing all possible funduscopic pathologies. Pre- and post-intervention testing was performed. Survey data assessing exposure to funduscopy skills during undergraduate and postgraduate training and overall experience with the simulator were collected. RESULTS: Most (65% [11/17]) participants reported minimal or no formal teaching in ophthalmology during their undergraduate medical studies. Average pre-intervention score (of 20) was 10.24±1.75 (51%) for the entire group, with no statistically significant difference between average pre-score in the control (10.63±1.77) versus intervention (9.89±1.76, p=0.405) groups. Intervention subjects experienced a statistically significant improvement in scores (9.89±1.76 vs. 12.78±2.05, p=0.006 [95% confidence interval 4.80-0.98]), but control subjects did not. CONCLUSIONS: A single session with an ophthalmoscopy simulator can improve diagnostic accuracy in postgraduate pediatric trainees. Use of ophthalmoscopy simulation represents a novel addition to traditional learning methods for postgraduate pediatric residents that can help trainees to improve their confidence and accuracy in performing this challenging examination.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".