Subjective Comfort and Physiology with Modern Contact Lens Care Products
Bibliographic record
Abstract
ABSTRACT Purpose To compare subjective comfort and ocular physiology with three multipurpose solutions (MPSs) to that of a peroxide‐based system with three different soft contact lens materials. Methods Habitual soft contact lens wearers (n = 236) were enrolled at three sites and completed a washout period with no contact lens solution for ≥4 days. Subjects were randomly assigned to one of three lens types: etafilcon A, galyfilcon A, or senofilcon A. A new lens of the assigned type was worn for 10 to 14 days each while using one of four care solutions, in random order (A—polyaminopropyl biguanide + polyquaternium, B—POLYQUAD + Aldox, C—alexidine + polyquaternium‐1, and D—hydrogen peroxide) with a washout period (≥4 days) between each solution. After each care solution, biomicroscopy was performed and subjective comfort was assessed using the Contact Lens User Experience (CLUE) questionnaire and other instruments including comfortable wear time (CWT). Linear mixed models were used for analysis. Comfort and biomicroscopy signs with each MPS were compared to that of the peroxide solution. Results Subjective CLUE Comfort score across all lens types with each MPS was not significantly different than with the peroxide solution (p = 0.98). There were no differences in CWT between each MPS and the peroxide solution for any lens type (range of differences: −0.8 to 0.8 h; all p ≥ 0.13). Six MPS/material combinations had no clinically meaningful change in corneal staining versus peroxide (<0.5 units); three combinations could increase staining by up to 0.57 units. Staining was Conclusions Comparable levels of comfort were found between the latest generation of MPSs compared to peroxide disinfection. Three MPS/material combinations tested could result in increased corneal staining of up to 0.57 units versus a peroxide solution. Overall, these data suggest the care systems investigated are generally appropriate for use with the contact lenses tested.
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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 | 0.001 | 0.002 |
| 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) | 0.003 | 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".