The muranga teleophthalmology study: Comparison of virtual (teleglaucoma) with in-person clinical assessment to diagnose glaucoma
Bibliographic record
Abstract
PURPOSE: While the effectiveness of teleophthalmology is generally accepted, its ability to diagnose glaucomatous eye disease remains relatively unknown. This study aimed to compare a web-based teleophthalmology assessment with clinical slit lamp examination to screen for glaucoma among diabetics in a rural African district. MATERIALS AND METHODS: Three hundred and nine diabetic patients underwent both the clinical slit lamp examination by a comprehensive ophthalmologist and teleglaucoma (TG) assessment by a glaucoma subspecialist. Both assessments were compared for any focal glaucoma damage; for TG, the quality of photographs was assessed, and vertical cup-to-disk ratio (VCDR) was calculated in a semi-automated manner. In patients with VCDR > 0.7, the diagnostic precision of the Frequency Doubling Technology (FDT) C-20 screening program was assessed. RESULTS: Of 309 TG assessment photos, 74 (24%) were deemed unreadable due to media opacities, patient cooperation, and unsatisfactory photographic technique. While the identification of individual optic nerve factors showed either fair or moderate agreement, the ability to diagnose glaucoma based on the overall assessment showed moderate agreement (Kappa [κ] statistic 0.55% and 95% confidence interval [CI]: 0.48-0.62). The use of FDT to detect glaucoma in the presence of disc damage (VCDR > 0.7) showed substantial agreement (κ statistic of 0.84 and 95% CI 0.79-0.90). A positive TG diagnosis of glaucoma carried a 77.5% positive predictive value, and a negative TG diagnosis carried an 82.2% negative predicative value relative to the clinical slit lamp examination. CONCLUSION: There was moderate agreement between the ability to diagnose glaucoma using TG relative to clinical slit lamp examination. Poor quality photographs can severely limit the ability of TG assessment to diagnose optic nerve damage and glaucoma. Although further work and validation is needed, the TG approach provides a novel, and promising method to diagnose glaucoma, a major cause of ocular morbidity throughout the world.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".