Distribution of Overnight Corneal Swelling Across Subjects With 4 Different Silicone Hydrogel Lenses
Bibliographic record
Abstract
PURPOSE: To determine distribution of central corneal swelling (CCS) across subjects after 8 hr of sleep in eyes wearing silicone hydrogel lenses with various oxygen transmissibility (Dk/t) values and in eyes without lenses. METHODS: Twenty-nine neophytes wore lotrafilcon A (Dk, 140), balafilcon A (Dk, 91), galyfilcon A (Dk, 60), and senofilcon A (Dk, 103) lenses in powers -3.00, -10.00, and +6.00 diopters on separate nights, in random order, and on 1 eye only. The contralateral eye (no lens) served as the control. Central corneal thickness was measured using a digital optical pachometer before lens insertion and immediately after lens removal on waking. RESULTS: The average difference between the mean (7%) and the median (6.8%) CCS of all lenses was only 0.2%, suggesting a normal distribution. There was no correlation between the mean and the range of the CCS (r=0.058, P=0.766). Normal CCS distributions were also found with each lens-wearing eye and the control eye (P>0.20 for all). There was a significant correlation between lens-wearing eye and control eye (r=0.895, P<0.001) and between lotrafilcon A and each of the other 3 lenses for mean CCS across the study participants (P<0.001 for all). CONCLUSIONS: Distribution of corneal swelling in both lens-wearing eye and control eye followed a normal curve. An individual's corneal swelling response seems to be independent of lens type.
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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".