SOCIAL DETERMINANTS OF HEALTH IN AGE-RELATED MACULAR DEGENERATION
Bibliographic record
Abstract
PURPOSE: To investigate the relationship between social determinants of health and the prevalence of age-related macular degeneration (AMD). METHODS: This analysis included adult respondents (≥50 years old) from the 2017 National Health Interview Survey. The primary outcomes were self-reported diagnosis of AMD and self-reported vision loss because of AMD. Univariable and multivariable logistic regression models were used for analysis. RESULTS: A total of 14,267 National Health Interview Survey participants were included, of whom 668 (4.7%) reported an AMD diagnosis. In the multivariable analysis, respondents aged over 81 years had higher odds of AMD (odds ratio [OR] = 7.54, 95% confidence interval [CI], 4.76-11.96, P < 0.001) compared with those aged 50 to 60. Divorced, separated, or widowed participants (OR = 1.27, 95% CI, 1.01-1.61, P = 0.042) also had a higher odds of AMD compared with married participants. Conversely, Black/African-American (OR = 0.23, 95% CI, 0.14-0.39, P < 0.001), Asian (OR = 0.38, 95% CI, 0.16-0.88, P = 0.023), and gay, lesbian, or bisexual respondents (OR = 0.45, 95% CI, 0.22-0.93, P = 0.032) had lower odds of AMD compared with White and heterosexual respondents, respectively. Employment was also associated with lower odds of AMD (OR = 0.71, 95% CI, 0.53-0.96, P = 0.026) compared with unemployment. CONCLUSION: Several social determinants of health were associated with self-reported AMD diagnosis. These factors should be considered by policymakers and clinicians to effectively orchestrate public health initiatives aimed at promoting equitable care.
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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 | 0.001 |
| 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) | 0.002 | 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".