Efficacy of Simulation-Based Learning for Direct and Indirect Ophthalmoscopy: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
This systematic review and meta-analysis evaluated the efficacy of simulation-based training for direct and indirect ophthalmoscopy. The primary outcome was successful fundus identification rate. Secondary outcomes included the simulation favorability rate, posttest results, and perceived benefits. Of the 955 studies identified, 11 studies were included, comprising a total sample of 772 participants. Simulation training improved fundus identification success, with 63.1% of participants correctly identifying fundus structures and demonstrated a high favorability rate, with 75.6% of participants preferring simulation over traditional training methods. Participants also demonstrated improvements in posttest knowledge and technical skills, with enhanced confidence, realism, and ease of learning, underscoring simulation's role in effectively advancing practical ophthalmic skills. Overall, simulation-based training seems effective in enhancing examination skills within simulated settings. These findings support its integration into ophthalmology curricula to enhance training outcomes. Interpretation of fundus identification outcomes should be cautious due to limited study numbers and assessment heterogeneity.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".