Age, Behavior, Environment, and Health Factors in the Soft Contact Lens Risk Survey
Bibliographic record
Abstract
PURPOSE: Previous studies have reported that the risk of corneal infectious and inflammatory events (CIEs) with soft contact lens (SCL) wear is highest in late adolescence and early adulthood. This study assesses the associations between patient age and other factors that may contribute to CIEs in young SCL wearers. METHODS: After ethics approvals and informed consent, a nonclinical population of young SCL wearers was surveyed in five US cities. Data from 542 SCL wearers aged 12-33 years were collected electronically. Responses were analyzed by age bins (12-14, 15-17, 18-21, 22-25, 26-29, and 30-33 years) using chi-square test. RESULTS: The cohort was 34% male and balanced across age bins. There were several significant associations between survey response and age (in bins). Wearers aged 18-21 years reported more recent nights with less than 6 hours of sleep (p < 0.001), more colds/flu (p = 0.049), and higher stress levels (p < 0.001). Wearers 18-21 and those 22-25 years were more likely to wear SCLs when showering (p < 0.001) and also reported more frequent naps with SCLs (p < 0.001). They reported sleeping in SCLs after alcohol use (p = 0.031), when traveling (p = 0.001), and when away from home (p = 0.024). Lower rates of regular hand washing before lens application (p = 0.054) was also associated with these groups. In addition, the relationship between reactive replacement and recommended replacement was dependent on age (p < 0.0001). CONCLUSIONS: Patient age influences lens wearing behaviors, environmental exposures, and other determinants of health that may contribute to increased CIEs in younger wearers. Targeted, age-specific education should be considered for both new and established SCL wearers.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".