Exploring ethnic and racial differences in intraocular pressure and glaucoma: The Canadian Longitudinal Study on aging
Bibliographic record
Abstract
Purpose: To determine whether self-reported race/ethnicity is associated with intraocular pressure (IOP) and glaucoma and to explore whether any associations are due to social, behavioral, genetic, or health differences. Design: Cross-sectional analysis of population-based data. Methods: We used the Canadian Longitudinal Study on Aging Comprehensive Cohort, which consists of 30,097 adults aged 45-85 years. Race/ethnicity was self-reported. Corneal-compensated intraocular pressure (IOP) was measured in mmHg using the Reichert Ocular Response Analyzer. Participants were asked to report if they have ever had a diagnosis of glaucoma and whether they used eye care in the past year. A glaucoma polygenic risk score (PRS) was calculated. Logistic and linear regression models were used. Results: Black individuals had higher mean IOP levels (beta coefficient (β) = 1.46; 95% confidence interval [CI], 0.62, 2.30) while Chinese, Japanese and Korean (β = -1.00; 95% CI, -1.63, -0.38) and Southeast Asian and Filipino individuals (β = -1.56; 95% CI, -2.68, -0.43) had lower mean IOP levels as compared to White individuals after adjustment for sociodemographic, behavioral, genetic, and health-related variables. Black people were more likely to report glaucoma as compared to White people after adjustment (odds ratio [OR] = 2.43; 95% CI, 1.27, 4.64). Conclusion: Racial and ethnic differences in IOP and glaucoma were identified. Adjusting for sociodemographic, behavioral, genetic, and health-related variables did not fully explain these differences. Longitudinal research is needed to further explore the reasons for these differences and to understand their relevance to disease pathogenesis and progression.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".