Impact of primary glaucoma on Health-Related Quality of Life in China： The Handan Eye Study
Bibliographic record
Abstract
Abstract Objectives: To assess health-related quality of life (HRQOL) by EQ-5D among glaucoma patients in the Handan Eye Study (HES), as well as the factors that influence the quality of life. Methods: A central clinic in the county hospital, a temporary clinic in the targeted villages, or at the participant’s home. The Handan Eye Study (HES) is a population-based prevalence study of eye disease in rural Yongnian County, northern China. A total of 99 adults with glaucoma were enrolled for analysis, including 67 with primary open-angle glaucoma (POAG) and 32 with primary angle-closure glaucoma (PACG). And 256 selected people with better visual acuity and visual field but Without Primary Glaucoma. Results of ophthalmic examinations and socio-economic information were recorded. HRQOL was measured using the EQ-5D, and visual function (VF) and vision-related quality of life (VRQOL) were evaluated using a Visual function-quality of life (VF-QOL) instrument. Primary and secondary outcome measures: EQ-5D and VF-QOL score. Results： The mean ± standard deviation (SD) scores on the EQ-5D, VF, and VRQOL for the 99 glaucoma cases were 0.98±0.04, 87.9±15.2, and 95.5±12.8 respectively. Utility values were significantly lower among participants with glaucoma (0.98±0.04) compared to those without (0.99±0.02, P = 0.008), even after adjusting for age, gender, education level, family income, and comorbidity (P = 0.02). There was a significantly lower utility value (0.92±0.08) among patients with lower VRQOL total score (55.4 ± 11.5) compared to higher (0.99 ± 0.03, P = 0.036), even after adjustment for age and family income (P = 0.006). Conclusion: Patients with glaucoma, particularly those with poor VRQOL, exhibited lower HR-QOL than those without. Early diagnosis and prevention facilitated by government health insurance may enhance VF-QOL for glaucoma patients, especially for PACG.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".