Smartphone Compatible versus Conventional Ophthalmoscope: A Randomized Crossover Educational Trial
Bibliographic record
Abstract
Abstract Objective The aim of the study is to compare performance and ease-of-use (EOU) of optic disk assessment using a smartphone direct ophthalmoscope attachment (D-EYE) to the gold standard direct ophthalmoscope (DO). Design The type of study involved is prospective, randomized, crossover, and educational trial. Participants The participants involved were first year medical students inexperienced in ophthalmoscopy. Methods Optic disks of standardized and volunteer patients were examined using the D-EYE and a conventional DO. Optic disk identification, EOU ratings of the devices, self-reported confidence level in their examination with the devices, and estimation of vertical cup-to-disk ratio (VCDR) were compared. Analyses included Chi-square tests, independent samples t-tests, correlations, and multivariable linear regression. Results Forty-four medical students voluntarily participated in the study. Students using the DO required more attempts (3.57 vs. 2.69, p = 0.010) and time (197.00 vs. 168.02 seconds, p = 0.043) to match the patient's fundus to the correct photograph. Overall EOU between the devices (6.40 vs. 4.79, p < 0.001) and overall confidence in examination (5.65 vs. 4.49, p = 0.003) were greater when using the D-EYE. There were no statistically significant differences in accuracy of VCDR estimations between the two ophthalmoscopes. Conclusion Smartphone ophthalmoscopy could offer additional learning opportunities in medical education and may be considered in clinical practice by non-specialist physicians given its greater EOU and increased success in visualizing the optic disk.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".