Glaucoma in the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA): cohort profile, prevalence, awareness and associations
Bibliographic record
Abstract
BACKGROUND/AIMS: This study aimed to describe the cohort profile of the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) and to report the prevalence of, awareness of and associations with glaucoma. METHODS: Using geographic stratification, a representative sample of non-institutionalised Northern Irish adults aged over 50 years was invited to participate. NICOLA participants underwent a Computer-Assisted Personal Interview (CAPI), a Self-Completion Questionnaire (SCQ) and a health assessment. The CAPI and SCQ collected comprehensive sociodemographic and health-related data. At the health assessment, participants underwent optic disc stereophotography, intraocular pressure (IOP) measurement using ocular response analyser (ORA), autorefraction, spectral domain optical coherence tomography and self-reported history of glaucoma. We invited NICOLA participants suspected of having glaucoma due to optic disc appearance or raised IOP for clinical examination by a glaucoma expert and perimetry. Epidemiological definitions by the International Society Geographical and Epidemiological Ophthalmology were used to define glaucoma. RESULTS: Of 3221 NICOLA participants (mean age 64.4, SD 8.5, female sex 51.7%) who attended the health assessment component of the NICOLA study (and had a vertical cup to disc ratio measurement in at least one eye), 91 participants had glaucoma. Overall, the crude prevalence of glaucoma was 2.83% (95% CI 2.31% to 3.46%) and 67% of affected individuals did not give a self-reported history of glaucoma. CONCLUSIONS: The prevalence of glaucoma in Northern Ireland is comparable with other population-based studies of European populations. Approximately two-thirds of people with glaucoma were undiagnosed.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".