Impact of a Rub and Rinse on Solution‐Induced Corneal Staining
Bibliographic record
Abstract
PURPOSE: To investigate whether the inclusion of a rub and rinse step before contact lens disinfection has an impact on solution-induced corneal staining. METHODS: This was a prospective, double-masked, single investigator study. Twenty participants were recruited for two visits, where balafilcon-A lenses were worn bilaterally for 2 h. Each pair of lenses was prepared using two different methodologies. The "control" lens was transferred from the blister pack directly into a storage case containing polyhexamethylene biguanide-based lens care solution. The contralateral "test" lens was rubbed and simultaneously rinsed using the same polyhexamethylene biguanide-based care solution, for either 60 s (visit 1) or 20 s (visit 2). Both lenses were then soaked in the solution overnight. After baseline corneal staining assessments, the lenses were inserted following a randomized contralateral model. After 2 h, lenses were removed, corneal staining was regraded, and comfort scores were obtained. RESULTS: Rubbed and rinsed test lenses induced significantly less corneal staining than control lenses for all participants during visit 1 (mean ± SD: 516 ± 843 vs. 2170 ± 902; p < 0.001) and visit 2 (522 ± 417 vs. 2091 ± 965; p < 0.001). There was no significant difference between the test lenses during visits 1 and 2 (p = 0.72) or controls (p = 0.50). Comfort scores did not differ between eyes (p > 0.05). CONCLUSIONS: Corneal staining induced after 2 h of lens wear with the combination of balafilcon-A and polyhexamethylene biguanide-based lens care solution can be significantly reduced by including a rub and rinse step before overnight soaking. Further work is required to establish the longevity of this effect during the monthly wearing cycle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".