Assessing the Risk Factors For Diagnosed Symptomatic Dry Eye Using a Smartphone App: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Dry eye (DE) is a chronic inflammatory disease of the ocular surface of the eye that affects millions of people throughout the world. Smartphone use as an effective health care tool has grown exponentially. The "Dry eye or not?" app was created to evaluate the prevalence of symptomatic DE, screen for its occurrence, and provide feedback to users with symptomatic DE throughout Thailand. OBJECTIVE: The purpose of this study was to compare the prevalence of symptomatic dry eye (DE), blink rate, maximum blink interval (MBI), and best spectacle-corrected visual acuity (BSCVA) between people with and without symptomatic DE and to identify risk factors for symptomatic DE in Thailand. METHODS: This cross-sectional study sourced data from the "Dry eye or not?" smartphone app between November 2019 and July 2020. This app collected demographic data, Ocular Surface Disease Index (OSDI) score, blink rate, MBI, BSCVA, and visual display terminal (VDT) use data. The criterion for symptomatic DE was OSDI score ≥13. RESULTS: The prevalence of symptomatic DE among individuals using this smartphone app in Thailand was 85.8% (8131/9482), with the Northeastern region of Thailand having the highest prevalence, followed by the Northern region. Worse BSCVA (median 0.20, IQR 0.40; P=.02), increased blink rate (median 18, IQR 16; P<.001), reduced MBI (median 8.90, IQR 10.80; P<.001), female sex (adjusted OR 1.83; 95% CI 1.59-2.09; P<.001), more than 6 hours of VDT use (adjusted OR 1.59; 95% CI 1.15-2.19; P=.004), and lower than bachelor's degree (adjusted OR 1.30; 95% CI 1.03-1.64; P=.02) were significantly associated with symptomatic DE. An age over 50 years (adjusted OR 0.77; 95% CI 0.60-0.99) was significantly less associated with symptomatic DE (P=.04). CONCLUSIONS: This smartphone DE app showed that the prevalence of symptomatic DE in Thailand was 85.8%. Signs and risk factors could be also evaluated with this smartphone DE app. Screening for DE by this app may allow for the development of strategic plans for health care systems in Thailand.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".