Assessing utility of a simple low-cost simulator model to improve direct ophthalmoscopy skills amongst medical students
Bibliographic record
Abstract
Abstract Background Ophthalmoscopy is an essential skill for medical trainees, however, trainees commonly report feeling uncomfortable with this skill. The purpose of this study is to determine the utility of a low-cost simulator in improving medical student’s comfort and proficiency with direct ophthalmoscopy. Methods An easy to assemble simulator was constructed using low-cost materials. First and second year medical students at the University of British Columbia were recruited. Participants in the Simulator group completed a survey to rate their competency using a direct ophthalmoscope prior to a practice session with the simulator. The Control group had no simulator session. Both groups then took part in the regularly scheduled clinical session and were assessed by a preceptor on their proficiency using an evidence-based rubric. The Simulator Group completed a post session survey about their perceived proficiency with an ophthalmoscope. Results The Simulator group had 14 participants and the Control group had 103 participants. Participants in the Simulator group self-reported significant increases (p < 0.05) in their perceived competence across all domains surveyed including overall comfort and visualizing the optic disc. There were no significant differences in proficiency rated by a preceptor between the Simulator and Control groups (7.21 vs 7.06 out of 10; p = 0.572). Conclusions This study demonstrates that a low-cost simulator is an accessible and valued method for improving student’s perceived competence and comfort in ophthalmoscopy. While this study did not show the simulator results in higher proficiency, rated by an evaluator, future studies can focus on more robust measures of skill acquisition and larger sample size.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".