Impact of Cosmetics on the Surface Properties of Silicone Hydrogel Contact Lenses
Bibliographic record
Abstract
PURPOSE: This study evaluated the impact of various cosmetics on the surface properties of silicone hydrogel (SiHy) contact lens materials. METHODS: In this in vitro experiment, 7 SiHy contact lens materials were coated with 1 of 9 cosmetics, including common hand creams (3), eye makeup removers (3), and mascaras (3). Dark-field microscopy images were taken to determine pixel brightness (PB) after cosmetic exposure, which describes the visible surface deposition (n=6 for each lens type), with a higher PB indicating increased deposition. The sessile drop technique was used to determine the advancing contact angle (CA). Measurements were repeated for both methods after a single peroxide-based cleaning cycle. RESULTS: Pixel brightness was significantly higher for mascara-coated lenses compared with the other cosmetic products (P<0.01). The peroxide-based lens care solution removed most deposits from the nonwaterproof mascara for 4 lens types, whereas deposits remained relatively unchanged for 1 waterproof mascara (P>0.05). Hand creams and makeup remover had minimal impact on PB. Changes in CA measurements after cosmetic application were highly lens dependent. Hand creams caused primarily a decrease in CA for 5 of the 7 lens types, whereas 1 of the waterproof mascaras caused a significant increase of 30 to 50° for 3 lens types. CONCLUSION: Some mascara-lens combinations resulted in increased CA and PB, which could have an impact on in vivo lens performance. Nonwaterproof mascara was mostly removed after a cleaning cycle. 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".