Results From the First Teleglaucoma Pilot Project in Addis Ababa, Ethiopia
Bibliographic record
Abstract
PRECIS: A teleglaucoma case-finding model was utilized in Ethiopia using a high-risk case identification approach. An overall 7.9% of patients had definite glaucoma, and 13.8% were glaucoma suspects. Most cases could be managed medically. BACKGROUND: This study was carried out to analyze disease prevalence and clinical referral pathways for high-risk patients assessed through a hospital-based teleglaucoma case-finding program. METHODS: Patients over the age of 35 years were referred from outpatient diabetic and hypertensive clinics. Through a teleglaucoma consultation, a glaucoma specialist provided remote diagnosis and management recommendations. Patient referral pathways were analyzed. Part way through the program, frontline ophthalmic nurses and optometrists were empowered to refer patients to be seen by general ophthalmologists within a week if patients met high-risk criteria. Qualitative stakeholder feedback was also obtained. RESULTS: A total of 1002 patients (53% female) were assessed with a mean age of 51.0±11.7 years. The prevalence of glaucoma and glaucoma suspects was 7.9% (79 cases) and 13.8% (138 cases), respectively. Retinopathy was found in 9.1%, with hypertensive retinopathy (2.7%) and diabetic retinopathy (2.5%) representing the majority of cases. Age-related macular degeneration was present in 1.5% and cataract in 16%. An overall 63% of cases were without organic eye disease. 35% of patients were referred to a general ophthalmologist, 0.7% to a glaucoma specialist (for surgery), 1.5% to a retina specialist, and 17.7% to an optometrist for further care. Qualitative analysis revealed that stakeholders felt the value of teleglaucoma would be in triaging patients requiring more urgent management and in identifying disease at an earlier stage. CONCLUSIONS: There is a high prevalence of glaucoma in Ethiopian patients assessed through this teleglaucoma program. This model and study have also demonstrated various principles behind telemedicine, such as the development of an intelligent triage system, case-finding for a variety of diseases, and consideration of optimal patient flow/referral pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".