Effectiveness of simulation models and digital alternatives in training ophthalmoscopy: A systematic review
Bibliographic record
Abstract
PURPOSE: Traditional direct ophthalmoscopy (TDO) is the oldest method of fundus examination; however, it has fallen out of use due to its technical difficulty and limitations to clinical utility, amidst the advent of potentially better options. A spectrum of new technologies may help in addressing the shortcomings of TDO: simulation mannequins with non-tracked TDO, simulation models with tracked TDO, and smartphone ophthalmoscopy (SFO). METHODOLOGY: A systematic search of PubMed, Embase, and Cochrane databases for all studies evaluating usage of simulation mannequins/models and SFO in ophthalmology education was performed, from inception till April 2023 with no language restriction. We ensured that we included all possible relevant articles by performing backward reference searching of included articles and published review articles. RESULTS: = 12). Non-tracked TDO and SFO were superior in training competency relative to control (TDO on real eyes). Intriguingly, tracked TDO was non superior to controls. SFO appears to enhance the learning effectiveness of ophthalmoscopy, due to real-time projection of the retina view, permitting instantaneous and targeted feedback. Learners reported improved ergonomics, including a wider field of view and more comfortable viewing distance. Retention of images and recordings permitted the audit of learning and paves the way for storage of such images in patients' electronic medical record and rapid dissemination for specialist referral. CONCLUSIONS: Smartphone ophthalmoscopy (SFO) permits integration of both the practice and learning of ophthalmoscopy, and the auditing of both. These advantages over traditional methods (with simulation or otherwise) may lead to a paradigm shift in undergraduate ophthalmology education. However, the nascency of SFO necessitates preservation of traditional techniques to tide through this period of transition.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".