Prevalence of dry eye disease in Ontario, Canada: A population-based survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Population-based cross-sectional survey in Ontario to estimate the 2016 prevalence of dry eye disease (DED) and associated risk factors among adults in Canada. METHODS: We emailed the 5-Item Dry Eye Questionnaire (DEQ-5) to 124,469 Ontario adults (age ≥18 years) in the IQVIA E360 database, March-April 2017. Inclusion criteria were: ≥2 visits to an Ontario based clinic, ≥1 visits in the 1 year before the study; database record with email. DED was defined as a DEQ-5 score of >6/22. The crude prevalence by age/sex of the Ontario sample was adjusted to the 2016 Canadian population (mean age 41.0 years, 51% female). Significance of DED risk factors (age, sex, selected diseases/medical conditions and medications) was evaluated by logistic regression analysis. RESULTS: Of the 5163 (4.1%) patients who completed the survey (59.5% female, median age, 46 years; 40.4% male, 56 years), 1135 respondents reported DED. Prevalence increased with age (p < 0.05) and was highest among those aged 55-64 years (24.7%; 95% CI, 22.1-27.3%) and lowest among those aged 25-34 years (18.4%; 95% CI, 15.9-21.0%). Prevalence was significantly higher (p < 0.001) among women (24.7%; 95% CI, 23.2-26.2%) than men (18.0%; 95% CI, 16.4-19.7%). Other risk factors were not significant. The age-/sex-adjusted Canadian DED prevalence estimate from this sample was 21.3% (95% CI, 19.8-23.2%), corresponding to ∼6.3 million people. CONCLUSIONS: Based on the Ontario sample, we estimate that >6 million Canadian adults may have DED, and that older people and females are more likely to be affected.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it