Detecting Glaucoma in Rural Kenya: Results From a Teleglaucoma Pilot Project in Nyamira, Kenya
Bibliographic record
Abstract
PRECIS: A teleglaucoma (TG) case-finding model was used in Kenya. Of the patients, 3.46% had definite glaucoma and 4.12% were glaucoma suspects. Most cases were of moderate to advanced stage and referred for further assessment. PURPOSE: The aim was to evaluate glaucoma prevalence in a high-risk population using a TG model. METHODS: Patients aged 35 or over were referred to the TG program from the outpatient diabetic and hypertensive clinics at Nyamira District Hospital (NDH) and from community awareness programs. Comprehensive ophthalmic examination included structured history, visual acuity, intraocular pressure, central corneal thickness, stereoptic nerve, and macular images. A glaucoma specialist provided diagnosis and management recommendation through virtual consultation. Glaucoma diagnosis and staging were based on at least 1 eye meeting the optic nerve criteria as specified by the Canadian glaucoma guidelines. RESULTS: In all, 1206 participants were seen and 19 of these could not complete the examination. Of 1187 patients, 56% were women and the mean age was 56.60±12.36 years. Of the patients, 11.8% had images that were ungradable in at least 1 eye. The prevalence of glaucoma and glaucoma suspects was 3.46% (n=42) and 4.12% (n=50), respectively. The proportion of patients with early, moderate, advanced, and absolute glaucoma was 2.4%, 33.3%, 52.4%, and 2.4%, respectively. Other diagnoses (pathology in at least 1 eye) included cataract in 13.2%, diabetic retinopathy in 1.48%, and optic atrophy in 1.98%. Of the patients, 28.2% were referred to the Innovation Eye Centre, Kisii, for further assessment. CONCLUSION: A structured TG program detected glaucoma in 3.46% of a rural Kenyan population. Timely patient referral was also initiated.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".