Magnitude and Determinants of Diabetic Retinopathy Among Indian Diabetic Patients Undergoing Telescreening in India
Bibliographic record
Abstract
Purpose: To determine the magnitude, determinants, and public health issues related to diabetic retinopathy (DR) in India using 2019 data from a for-profit telescreening program. Methods: Digital retinal images were captured using a nonmydriatic fundus camera and transferred via the telescreening program to a reading center. Ophthalmologists trained in DR image reading created the DR status reports. Age/sex-adjusted rates of DR, sight-threatening DR (STDR), and diabetic macular edema (DME) were calculated and correlated with known risk factors. Results: Images of 51,760 Indian diabetic patients (103,520 eyes) were reviewed. The prevalence of DR, STDR, and DME was 19.1% (95% confidence interval [CI]: 18.9–19.5), 5.1% (95% CI: 4.9–5.3), and 3.9% (95% CI: 3.7–4.1), respectively. Based on these data, we projected 14.7 million cases of DR, 3.9 million with STDR, and 3.0 million DME cases in India. Statistically significant risk factors for DR were male gender (odds ratio [OR] = 1.19, p < 0.001), older age (χ2 = 270, df = 3, p < 0.001), history of cataract surgery (OR = 2.0, p < 0.001), longer duration of diabetes (χ2 = 1084, p < 0.001), and type 1 diabetes (OR = 3.9, p = 0.01). There was a statistically significant variation of DR by geographic zones (χ2 = 310, p < 0.001). Laser treatment coverage for STDR was 22%. Duration of diabetes (p < 0.001), cataract surgery in the past (p = 0.02), and females (p = 0.001) were predictors of STDR. Conclusion: This model of telescreening for DR provides an additional pathway for screening and preventing diabetes-related visual morbidity in India. The data from this study can be used for epidemiologic and ophthalmic health policies related to diabetes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".