Simulation models in direct ophthalmoscopy education: a systematic review
Bibliographic record
Abstract
Background: An ever-increasing range of simulation devices are available for direct ophthalmoscopy. However, the effectiveness of simulation design and components have not been evaluated. This systematic review aims to describe and evaluate direct ophthalmoscopy simulation models and highlight components that have been found to be effective, and challenges faced when using simulation models. Methods: A systematic review of the literature was conducted according to the PRISMA statement in four online databases: Medline, Embase, Cochrane Library and Web of Science. Citation searching using Google Scholar and Citationchaser was also undertaken. Validity and effectiveness were assessed using a validated scale based on Messick's modern validity framework and McGaghie's proposed levels of simulation-based translational outcomes respectively. Results: A total of 1,275 titles and abstracts were screened. A total of 37 studies were included in the final analysis. Physical models, digital models and virtual reality direct ophthalmoscopy models were described in studies. A plastic cannister design was the most common in the literature, followed by a sphere with a painted fundus and the EyeSi Direct Ophthalmoscope Simulator (VRmagic, GmbH, Mannheim, Germany). Simulation was effective in its ability to allow students to engage in repeated practice without patient discomfort. The lack of realism was the most noted limitation of simulation practice. Conclusion: While more robust evidence is needed to support simulation design efficacy in direct ophthalmoscopy, simulation-based teaching of direct ophthalmoscopy will likely be increasingly effective as technological advancements support improved realism and affordability.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".