Impact of Protein and Lipid on Neutralization Times of Hydrogen Peroxide Care Regimens
Bibliographic record
Abstract
PURPOSE: To investigate the effect of protein, lipid, and lens material on the neutralization kinetics of one-step hydrogen peroxide disinfection systems. METHODS: A UV-based assay was used to determine the rate of neutralization of three one-step hydrogen peroxide systems (CIBA Vision Clear Care; CIBA Vision AOSEPT; Abbott Medical Optics UltraCare). Protein (bovine serum albumin and lysozyme) and various lipids were added to the lens cases during the neutralization phase to determine whether they influenced the rate of neutralization. Finally, rates were determined when the cases contained a silicone hydrogel lens material (lotrafilcon A) or Food and Drug Administration group IV (etafilcon A) lenses. RESULTS: Neutralization for all three systems was complete within 90 minutes. The rate of neutralization for Clear Care and AOSEPT were not significantly different from each other (P=NS). UltraCare exhibited statistically higher levels of peroxide up to the 20-minute time point (P<0.001) Protein, lipid, or lens material did not significantly affect the rate of neutralization for any regimen (P=NS). CONCLUSIONS: Tablet-based one-step disinfection systems neutralize at a slower rate than disc-based peroxide systems, but this difference is only significant during the first 20 minutes after the onset of neutralization. Neither lens deposition nor lens material plays a role in the speed of neutralization of peroxide-based systems.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".