Higher Occurrence of Ocular Surface Disease Symptoms in Patients with Atopic Dermatitis: Data from 57,464 Subjects in the FOREVER Cohort
Bibliographic record
Abstract
Abstract: Background: Ocular comorbidity in atopic dermatitis (AD) is known, but ocular surface disease (OSD) symptoms do not always result in a physiciańs diagnosis. Objective: To investigate the occurrence of OSD symptoms and self-reported vision and health in a general population and explore their association with AD. Methods: A cross-sectional questionnaire study using the nationwide population-based FOREVER cohort of Danish adults. The occurrence of OSD symptoms, self-reported vision and health, and their association with AD were investigated using logistic regression models. Results: A total of 57,464 (85%) out of 67,178 participants completed the questionnaire. 1,298 had current AD, 5,198 had a history of AD, and 50,968 never had AD. The occurrence of one or more OSD symptoms was associated with both current (adjusted odds ratio, 1.19; 95% confidence interval, 1.05–1.34; P = 0.006) and previous AD (1.16; 1.09–1.23; P < 0.001), compared to those who never had AD, after adjusting for age, sex, smoking status, and atopic comorbidities (asthma and rhinitis). Participants with current and previous AD also rated their overall health “worse” (1.29; 1.16–1.43; P < 0.001) (1.12; 1.06–1.18; P < 0.001), compared to those who never had AD. Conclusions: The occurrence of OSD symptoms and worse self-rated vision and health are associated with AD in a general population.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".