Nurse-led diabetic retinopathy screening: a pilot study to evaluate a new approach to vision care for Canadian Aboriginal peoples
Bibliographic record
Abstract
Diabetic retinopathy is the most common cause of new cases of blindness and is pandemic among Aboriginal people around the world. To reduce health inequities, accessible vision screening among these high-risk populations is essential. To assess cardio-metabolic co-morbidities associated with type 2 diabetes and the use of a portable fundus camera as a novel approach for convenient, earlier and more accessible vision screening for Aboriginal peoples living with type 2 diabetes in northern and remote Canadian communities. This quantitative pilot study screened participants diagnosed with type 2 diabetes for commonly associated cardio-metabolic co-morbidities using anthropometrical measurements, blood pressure and a A1c (HbA1c) blood glucose test, followed by vision exams conducted first by a trained nurse and then by an ophthalmologist to screen for signs of retinopathy using fundus photography. Large numbers of the participants presented with overweight/obese (84.8%), pre-hypertension/hypertension (69.7%) and an elevated A1C (78.8%). Inter-rater reliability demonstrated substantial agreement between vision exam judgements made by the nurse and ophthalmologist (k = .67). Nurse-led vision screening in remote or northern communities can improve the standard of care by extending access to health services, lowering the costs to families by reducing travel expenses and preventing vision loss in a family member.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 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) | 0.003 | 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".