Multicenter Testing of a Risk Assessment Survey for Soft Contact Lens Wearers With Adverse Events: A Contact Lens Assessment in Youth Study
Bibliographic record
Abstract
PURPOSE: To test the ability of responses to the Contact Lens Assessment in Youth (CLAY) Contact Lens Risk Survey (CLRS) to differentiate behaviors among participants with serious and significant (S&S) contact lens-related corneal inflammatory events, those with other events (non-S&S), and healthy controls matched for age, gender, and soft contact lens (SCL) wear frequency. METHODS: The CLRS was self-administered electronically to SCL wearers presenting for acute clinical care at 11 clinical sites. Each participant completed the CLRS before their examination. The clinician, masked to CLRS responses, submitted a diagnosis for each participant that was used to classify the event as S&S or non-S&S. Multivariate logistic regression analyses were used to compare responses. RESULTS: Comparison of responses from 96 participants with S&S, 68 with non-S&S, and 207 controls showed that patients with S&S were more likely (always or fairly often) to report overnight wear versus patients with non-S&S (adjusted odds ratio [aOR], 5.2; 95% confidence interval [CI], 1.4-18.7) and versus controls (aOR, 5.8; CI, 2.2-15.2). Patients with S&S were more likely to purchase SCLs on the internet versus non-S&S (aOR, 4.9; CI, 1.6-15.1) and versus controls (aOR, 2.8; CI, 1.4-5.9). The use of two-week replacement lenses compared with daily disposables was significantly higher among patients with S&S than those with non-S&S (aOR, 4.3; CI, 1.5-12.0). Patients with S&S were less likely to regularly discard leftover solution compared with controls (aOR, 2.5; CI, 1.1-5.6). CONCLUSIONS: The CLRS is a clinical survey tool that can be used to identify risky behaviors and exposures directly associated with an increased risk of S&S events.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".