Magnitude and Determinants of Ocular Morbidities Among Persons with Diabetes in a Project in Ahmedabad, India
Bibliographic record
Abstract
BACKGROUND: Visual disabilities due to diabetes are on the rise, especially in urban areas of developing countries. Proper health planning will need evidence-based information. STUDY DESIGN AND METHODS: We estimated the prevalence and identified the determinants of eye complications among persons with diabetes screened in Ahmedabad, India, during 2007-2008. This was a review of the data from a health institution-based project. Physicians collected information on diabetes, and ophthalmologists examined the patients for visual acuity, diabetic retinopathy (DR), glaucoma, and cataracts. World Health Organization-recommended grading of DR was used. Frequencies, prevalence, and 95% confidence interval (CI) values were calculated. RESULTS: Of 40,919 persons who we examined for diabetes, 9,246 (66.6%) persons knew that they had diabetes, whereas 4,641 (33.4%) persons were detected with diabetes for the first time. The prevalence of DR, early cataract, and glaucoma among those who knew that they had diabetes was 14.6% (95% CI 13.9-15.3), 44.4% (95% CI 43.4-45.4), and 5.4% (95% CI 4.9-5.9), respectively. The prevalence of DR among persons with diabetes (new and old) was 10.1% (95% CI 9.6-10.6). Although poor vision was positively associated with DR (chi2 = 706), 40% of those with DR had vision better than 20/60. Male sex (odds ratio [OR] = 1.31), longer duration of diabetes (chi2 = 1,808), hypertension (OR = 1.13), good sugar control (OR = 0.09), and nephropathy (OR = 2.16) were the factors associated with DR. Regression analysis suggested that longer duration of diabetes and poor control of diabetes were the predictors of DR. CONCLUSIONS: The prevalence of DR was low. Long duration of diabetes, poor control of blood sugar, presence of nephropathy, and hypertension were associated with DR. Good vision could mislead about the severity of DR.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".