Screening for diabetic retinopathy in James Bay, Ontario: a cost-effectiveness analysis.
Bibliographic record
Abstract
BACKGROUND: Retinopathy is a common complication of diabetes mellitus that if detected early by regular retinal examinations can be treated; thus, blindness can be delayed or prevented. Providing high-quality retinal screening is difficult, especially in rural and remote areas. Canada's First Nations population has a higher prevalence of diabetes and is, in general, more geographically isolated than the broader Canadian population. We modelled the cost-effectiveness of retinopathy screening by travelling retina specialists versus retinal photography with a portable digital camera in an isolated First Nations cohort with diabetes. METHODS: The 2 screening programs were modelled to run concurrently for 5 years, with outcomes evaluated over 10 years. To construct economic models for the population of Cree individuals living in the western James Bay area of northern Ontario, we used available data on the prevalence of diabetes in the area and estimates of the incidence of retinopathy derived from the published literature. We compared the screening models and calculated total costs, visual outcome, costs per sight-year saved and costs per quality-adjusted life year (QALY). We also estimated the costs of implementing a screening program for all First Nations individuals in Ontario with diabetes and no access to an ophthalmologist. RESULTS: From the perspective of the health care system the camera program was preferable to the specialist-based program. Over 10 years, 67 v. 56 sight years were saved, compared with no screening, at costs of 3900 Canadian dollars v. 9800 Canadian dollars per sight year and 15,000 Canadian dollars v. 37,000 Canadian dollars per QALY. Generalizing these results to the province of Ontario, the camera system could allow most isolated First Nations people with diabetes to be screened for 5 years for approximately 1.2 million Canadian dollars. INTERPRETATION: A portable retinal camera is a cost-effective means of screening for diabetic retinopathy in isolated communities of at-risk individuals.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".