SOCIODEMOGRAPHIC FACTORS AS PREDICTORS OF DIABETIC RETINOPATHY SCREENING
Bibliographic record
Abstract
PURPOSE: This systematic review investigates how sociodemographic factors influence diabetic retinopathy (DR) screening adherence among individuals with diabetes. The review examines individuals with diabetes as the target population, focusing on the impact of various sociodemographic exposures on DR screening uptake. METHODS: A comprehensive systematic search was conducted across Ovid MEDLINE, Embase, and the Cochrane Library from inception to November 2024. The primary outcome was the overall rate of DR screening among individuals, while secondary outcomes included the odds ratios or proportions of individuals screened for DR, stratified by sociodemographic factors. RESULTS: Thirty-three studies were included, spanning more than 100,000 participants. Older age, higher education, higher income, and private insurance were consistently associated with higher screening adherence. Employed individuals, particularly those in manual labor or with rigid schedules, had lower participation. Women generally showed higher adherence, although findings varied. Ethnic disparities were observed, with Black and Hispanic populations demonstrating lower screening rates. Geographic distance and travel burden were frequently reported barriers. CONCLUSION: This review demonstrates that sociodemographic factors significantly affect DR screening adherence. Strengths include the broad geographic scope and diversity of populations studied. Limitations involve study heterogeneity and occasional reliance on self-reported data.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".