Water Exposure is a Common Risk Behavior Among Soft and Gas-Permeable Contact Lens Wearers
Bibliographic record
Abstract
PURPOSE: To understand soft contact lens (SCL) and gas-permeable (GP) lens wearers' behaviors and knowledge regarding exposure of lenses to water. METHODS: The Contact Lens Risk Survey (CLRS) and health behavior questions were completed online by a convenience sample of 1056 SCL and 85 GP lens wearers aged 20 to 76 years. Participants were asked about exposing their lenses to water and their understanding of risks associated with these behaviors. Chi-square analyses examined relationships between patient behaviors and perceptions. RESULTS: GP lens wearers were more likely than SCL wearers to ever rinse or store lenses in water (rinsing: 91% GP, 31% SCL, P < 0.001; storing: 33% GP, 15% SCL P < 0.001). Among SCL wearers, men were more likely to store (24% vs. 13%, P = 0.003) or rinse (41% vs. 29%, P = 0.012) their lenses in water. Showering while wearing lenses was more common in SCL wearers (86%) than GP lens wearers (67%) (P < 0.0001). Swimming while wearing lenses was reported by 62% of SCL wearers and 48% of GP lens wearers (P = 0.027). Wearers who rinsed (SCL; P < 0.0001, GP; P = 0.11) or stored lenses in water (SCL; P < 0.0001, GP P = 0.007) reported that this behavior had little or no effect on their infection risk, compared with those who did not. Both SCL (P < 0.0001) and GP lens wearers (P < 0.0001) perceived that distilled water was safer than tap water for storing or rinsing lenses. CONCLUSIONS: Despite previously published evidence of Acanthamoeba keratitis' association with water exposure, most SCL, and nearly all GP lens wearers, regularly expose their lenses to water, with many unaware of the risk.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".