Age and Other Risk Factors for Corneal Infiltrative and Inflammatory Events in Young Soft Contact Lens Wearers from the Contact Lens Assessment in Youth (CLAY) Study
Bibliographic record
Abstract
PURPOSE: To describe age and other risk factors for corneal infiltrative and inflammatory events (CIEs) in young, soft contact lens (SCL) wearers and to model the age-related risk. METHODS: A multicenter, retrospective chart review of 3549 SCL wearers (8-33 years at first observed visit, +8.00 to -12.00D, oversampling <18 years) captured CIEs from January 2006 to September 2009. The review noted age, sex, SCL worn, use of lens care products, and SCL wearing history. Event diagnoses were adjudicated to consensus by reviewers masked to wearer identity, age, and SCL parameters. Significant univariate risk factors for CIEs were subsequently tested in multivariate generalized estimating equations. RESULTS: Charts from 14,305 visits observing 4,663 SCL years yielded 187 CIEs in 168 wearers. Age was a significant nonlinear risk factor, peaking between 15 and 25 years (P < 0.008). Less than 1 year of SCL use was protective versus longer years of wear (P < 0.0003). Use of multipurpose care products (2.86×), silicone hydrogels (1.85×), and extended wear (2.37×) were significantly associated with CIEs in the multivariate model (P < 0.0001 each). CONCLUSIONS: Patient age, years of lens wear, use of multipurpose care products, silicone hydrogels, and extended wear were all significantly associated with CIEs with SCL wear. Use of SCLs in young patients aged 8 to 15 years was associated with a lower risk of infiltrative events compared with teens and young adults. In terms of safety outcomes, SCLs appear to be an acceptable method of delivering optics designed to manage myopia progression in children and young teens in the future.
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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 | 0.001 |
| 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".