Rewetting Drops Containing Surface Active Agents Improve the Clinical Performance of Silicone Hydrogel Contact Lenses
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the impact of using a rewetting drop (RWD) containing surface active agents (OPTI-FREE RepleniSH; Alcon, Fort Worth, TX) on the clinical performance and protein deposition when using a continuous-wear (CW) silicone hydrogel (SH) contact lens. METHODS: Subjects wore lotrafilcon A SH lenses on a 30-day CW basis for two consecutive 1-month periods while inserting either 0.9% unpreserved unit-dose saline (control) or multidose OPTI-FREE RepleniSH (test RWD). Subjective comfort and symptoms were assessed after 2 and 4 weeks with each product. After 1 month of wear with each product, lenses were collected and analyzed in the laboratory for total protein, total lysozyme, and percentage of denatured lysozyme. RESULTS: Symptoms of dryness and comfort varied across the day regardless of drop type (p < 0.001) with dryness being maximal on waking, least in the middle of the day, and increased towards the evening. The test RWD provided greater comfort on insertion (p = 0.02), better visual quality (p < 0.01), and less mucous discharge on waking (p = 0.02) than the control product. Lysozyme deposition was significantly reduced after the use of the test RWD as compared to saline (0.73 +/- 0.5 microg/lens vs. 1.14 +/- 0.7 microg/lens; p < 0.001) as was total protein deposition (1.17 +/- 0.7 microg/lens vs. 1.86 +/- 0.8 microg/lens; p < 0.001). Lysozyme denaturation was also reduced with the use of the test RWD compared with the control (76 +/- 10% vs. 85 +/- 7%; p < 0.01). CONCLUSIONS: The use of a RWD containing surface active agents provided greater subjective satisfaction, reduced lysozyme and total protein deposition, and reduced denatured lysozyme than a RWD containing saline alone.
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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) | 0.000 | 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) | 0.001 | 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".