Modeling the Prevalence of Age‐Related Cataract: Waterloo Eye Study
Bibliographic record
Abstract
PURPOSE: To report on the prevalence of age-related (AR) cataract in an optometric clinic population including male and female subgroups. METHODS: Retrospective patient file data reviewed for the Waterloo Eye Study database included age, sex, date of lens extraction (LE), and presence of AR cataract [nuclear sclerosis (NS), cortical cataracts (CC), posterior subcapsular (PSC) or associated LE]. Prevalence (%) was calculated for overall AR cataract, NS, CC, PSC, and bilateral LE for all Waterloo Eye Study patients. Logistic regression analysis was used to create age functions for overall AR prevalence and for significant differences in cataract types for males and females. The distribution of homogeneous and mixed cataract and mean age of first LE were determined for males and females. RESULTS: The prevalence of all AR, NS, CC, PSC, and bilateral LE was 35.3, 28.8, 9.9, 3.6, and 14.0%, respectively. Being female was associated with an increased prevalence of CC (odds ratio = 1.54, 95% confidence interval, 1.27 to 1.88) and bilateral LE (odds ratio = 1.41, 95% confidence interval, 1.09 to 1.84). Females reached 50% prevalence earlier than men for CC (76.7 vs. 82.6 years, p 0.05) and bilateral LE (84.6 vs. 90.5 years, p 0.05). Males had an earlier age of first LE than females (70.4 vs. 73.2 years; p 0.01). CONCLUSIONS: Logistic regression modeling indicates that being female in this optometric clinic population was associated with an increased prevalence of CC, mixed cataract, surgical intervention, and later age of first LE. These data are important for public health planning.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".