Outreach screening to address demographic and economic barriers to diabetic retinopathy care in rural China
Bibliographic record
Abstract
IMPORTANCE: Poor access to existing care for diabetic retinopathy (DR) limits effectiveness of proven treatments. OBJECTIVES: We examined whether outreach screening in rural China improves equity of access. DESIGN, SETTING AND PARTICIPANTS: We compared prevalence of female sex, age > = 65 years, primary education or below, and requiring referral care for DR between three cohorts with diabetes examined for DR in neighboring areas of Guangdong, China: passive case detection at secondary-level hospitals (n = 193); persons screened during primary-level DR outreach (n = 182); and individuals with newly- or previously-diagnosed diabetes in a population survey (n = 579). The latter reflected the "ideal" reach of a screening program. RESULTS: Compared to the population cohort, passive case detection reached fewer women (50·8% vs. 62·3%, p = 0·006), older adults (37·8% vs. 51·3%, p < 0·001), and less-educated persons (39·9% vs. 89·6%, p < 0·001). Outreach screening, compared to passive case detection, improved representation of the elderly (49·5% vs. 37·8%, p = 0·03) and less-educated (70·3% vs. 39·9%, p<0·001). The proportion of women (59.8% vs 62.3%, P>0.300) and persons aged > = 65 years (49.5% vs 51.3%, p = 0.723) in the outreach screening and population cohorts did not differ significantly. Prevalence of requiring referral care for DR was significantly higher in the outreach screening cohort (28·0%) than the population (14·0%) and passive case detection cohorts (7·3%, p<0·001 for both). CONCLUSIONS AND RELEVANCE: Primary-level outreach screening improves access for the poorly-educated and elderly, and removes gender inequity in access to DR care in this setting, while also identifying more severely-affected patients than case finding in hospital.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".