Impact of Cosmetics on the Physical Dimension and Optical Performance of Silicone Hydrogel Contact Lenses
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact of cosmetics on silicone hydrogel (SiHy) contact lens shape, lens power, and optical performance. METHODS: In this in vitro experiment, 7 SiHy materials were coated with 9 marketed brands of cosmetics, including hand creams (HCs) (3), eye makeup removers (MRs) (3), and mascaras (3). Diameter, sagittal depth, and base curve were determined using the Chiltern (Optimec Limited), whereas lens power and optical performance were assessed using the Contest Plus (Rotlex). Six replicates were used for each lens and cosmetic combination. Measurements were repeated after a cleaning cycle using a one-step hydrogen peroxide solution. RESULTS: Makeup removers had the greatest impact on diameter, sagittal depth, and base curve, resulting in changes of up to 0.5, 0.15, and 0.77 mm, respectively. The HCs and mascaras had little impact on these parameters; however, differences were observed between lens types. Optical performance was reduced with all mascaras, and a decrease of greater than 2 units on a 0 to 10 scale (10=uniform power distribution) was seen for 5 lens types exposed to waterproof mascara (P<0.01). Most HCs and MRs had minimal impact on image quality. Lens power did not change with any of the cosmetics (± 0.25 diopter; P>0.05). Lens cleaning resulted in some recovery of the lens parameters, and efficiency varied between cosmetics. CONCLUSION: Some eye MRs and waterproof mascaras changed the shape and optical performance of some SiHy lenses. Further research is needed to understand the clinical implications for SiHy lens wearers using cosmetics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".