Impact of social determinants of health and medication burden on quality of life and disease severity in glaucoma
Bibliographic record
Abstract
BACKGROUND: The interplay between social determinants of health (SDH), peripheral field loss in glaucoma, frailty, and medication burden impacts treatment outcomes and quality of life (QoL). OBJECTIVE: To evaluate the interaction between SDH, glaucoma characteristics, frailty, QoL in an Atlanta cohort with peripheral field loss. METHODS: Cross-sectional study including primary glaucoma subjects satisfying inclusion criteria. Electronic medical records provided age, zip code (for area deprivation, crime rate, poverty), visual acuity, binocular Visual Field Index (OU VFI), and number of medications. Glaucoma severity was classified by OU VFI: mild (>95%), mild to moderate (81-95%), moderate to severe (50-80%), and severe (<50%). Subjects completed surveys on demographics (race, sex, income, occupation, education), QoL (Short Form-36 and National Eye Institute-Visual Function Questionnaires), and frailty (FRAIL questionnaire). RESULTS: Among 139 subjects (mean age 67 ± 11 years), glaucoma severity was greater in Black versus White individuals (OU VFI: 72 ± 25% vs 83 ± 19%; p = 0.01), and in men versus women (OU VFI: 72 ± 26% vs 83 ± 19%; p = 0.004). Severity correlated with visual acuity (ANOVA: p < 0.001); QoL (NEI-VFQ scores: r = 0.6; p < 0.001). Neither zip code indices nor frailty were associated with severity/QoL. Multivariate modeling identified glaucoma severity as the sole predictor of QoL; medication burden had no independent impact. CONCLUSIONS: Black race, male sex, visual acuity, and medication burden were associated with glaucoma severity. QoL was associated solely with glaucoma severity; medication burden had no independent impact. These findings inform glaucoma management strategies in Atlanta's diverse community.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".