Prevalence and risk factors for diabetic retinopathy in China: a multi-hospital-based cross-sectional study
Bibliographic record
Abstract
PURPOSE: To determine the prevalence and risk factors for diabetic retinopathy (DR) and sight-threatening diabetic retinopathy (STDR) in a multi-hospital-based DR screening programme among patients with diabetes in China, the Lifeline Express Diabetic Retinopathy Screening Program. METHODS: Patients with diabetes in eight hospitals across mainland China (both southern and northern) from January 2014 to July 2016 were recruited in this programme. All participants underwent a standardised interview and examinations and received digital fundus photography. DR severity was graded from retinal fundus photographs by retina specialists in the reading centre of Joint Shantou International Eye Center, according to the grading standards of the English National Screening Programme. STDR was defined as the presence of preproliferative DR (R2), proliferative DR (R3) and/or maculopathy (M1). RESULTS: 16 305 patients with diabetes were screened for DR in total. Fundus photographs were gradable for 15 078 patients (92.5%). The age-gender-standardised prevalence of any DR and STDR was 27.9% (95% CI, 27.2% to 28.6%) and 12.6% (95% CI, 12.1% to 13.1%), respectively. In the multiple logistic regression analysis, younger age (OR, 0.967), longer duration of diabetes (OR, 1.093), higher haemoglobin A1c (OR, 1.115), higher fasting plasma glucose (OR, 1.074), higher systolic blood pressure (OR, 1.014), faster heart rate (OR, 1.010), higher low-density lipoprotein (OR, 1.149), lower triglycerides (OR, 0.975), higher blood urea nitrogen (BUN) (OR, 1.012) and elevated serum creatinine level (OR, 1.003) were associated with the presence of DR. Similar risk factors, except for BUN and triglycerides, were found for STDR. CONCLUSIONS: The prevalence of DR and STDR in diabetes was 27.9% and 12.6%, respectively in this multi-hospital-based population across China. Compared with Western population with diabetes, similar risk factors for DR and STDR were found in Chinese patients with 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".