Contact Lens Assessment in Youth: Methods and Baseline Findings
Bibliographic record
Abstract
PURPOSE: To describe the Contact Lens Assessment in Youth (CLAY) Study design and report baseline data for a multicenter, retrospective, observational chart review of children, teenagers, and young adult soft contact lens (SCL) wearers. METHODS: Clinical charts of SCL wearers aged 8 to 33 years were reviewed at six colleges of optometry. Data were captured retrospectively for eye care visits from January 2006 through September 2009. Patient demographics, SCL parameters, wearing schedules, care systems, and biomicroscopy findings and complications that interrupted SCL wear were entered into an online database. RESULTS: Charts from 3549 patients (14,276 visits) were reviewed; 78.8% were current SCL wearers and 21.2% were new fits. Age distribution was 8 to <13 years (n = 260, 7.3%), 13 to <18 years (n = 879, 24.8%), 18 to <26 years (n = 1,274, 36.0%), and 26 to <34 years (n = 1,136, 32.0%). The sample was 63.2% females and 37.7% college students. At baseline, 85.2% wore spherical SCLs, 13.5% torics, and 0.1% multifocals. Silicone hydrogel lenses were worn by 39.3% of the cohort. Daily wear was reported by 82.1%, whereas 17.9% reported any or occasional overnight wear. Multipurpose care systems were used by 78.1%, whereas another 9.9% indicated hydrogen peroxide solutions use. CONCLUSIONS: This data represent the SCL prescribing and wearing patterns for children, teenager, and young adult SCL wearers who presented for eye care in North American academic clinics. This will provide insight into SCL utilization, change in SCL refractive correction, and risk factors for SCL-related complications by age group.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".