The muranga teleophthalmology study: A comparison of virtual (teleretina) assessment with in-person clinical examination to diagnose diabetic retinopathy and age-related macular degeneration in kenya
Bibliographic record
Abstract
PURPOSE: This study compares a web-based teleophthalmology assessment with a clinical slit lamp examination to screen for diabetic retinopathy (DR) and age-related macular degeneration (AMD) among diabetic patients in a rural East African district. METHODS: Six hundred and twelve eyes from 306 diabetic patients underwent both a clinical slit lamp examination and a teleretina (TR) assessment by an experienced ophthalmologist. Both assessments were compared for any DR and AMD using the early treatment diabetic retinopathy study and age-related eye disease study grading scales, respectively. RESULTS: Of the 612 TR assessment photos, 74 (12%) were deemed ungradable due to media opacities, poor patient cooperation, or unsatisfactory photographs. The ability to detect DR and AMD showed a fair agreement (kappa statistic 0.27 and 0.23, respectively) between the TR and clinical slit lamp examination. Relative to a clinical slit lamp evaluation, a positive TR diagnosis carried a 75.0% positive predictive value when diagnosing DR and a 27.3% positive predictive value when diagnosing AMD. A negative TR diagnosis carried a 97.2% negative predictive value for the diagnosis of DR and a 98.1% negative predictive value for the diagnosis of AMD. CONCLUSION: When comparing TR assessments to clinical slit lamp examinations to diagnose DR and AMD, there was a fair agreement. Although further validation is needed, the TR approach provides a promising method to diagnose DR and AMD, two major causes of ocular impairment worldwide.
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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 | 0.006 |
| 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 | 0.000 |
| 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".