Mixed Model Analysis of Between-Subject Variability in Overnight Corneal Swelling and Deswelling With Silicone Hydrogel Lenses
Bibliographic record
Abstract
Purpose: To model between subject variability of corneal swelling (CS) and deswelling after overnight wear of silicone hydrogel (SiHy) contact lenses. Methods: A total of 29 neophyte subjects wore 12 SiHy lenses with central transmissibility range of 31 to 211 Dk/t units on separate nights, in random order, and on one eye only. The contralateral eye served as the control. Central corneal thickness was measured using digital optical pachymetry before lens insertion, immediately after lens removal on waking, then 20, 40 minutes, 1, 2, and 3 hours later. Mixed modelling was conducted for simultaneous analysis of group and between-subject effects of CS and deswelling. Results: The best model for overnight CS versus Dk/t was linear with a random intercept showing constant between-subject differences in CS for different Dk/t values. The best fit for corneal deswelling versus time was a curvilinear random intercept and random slope model. About 90% of the total between-subject deswelling variance in either lens or control eyes was due to the intercept variability with much less (∼10%) being due to the variability of the individual deswelling rate (slope). Subject age, sex, and ametropia were not predictors of individual corneal swelling in the swelling versus Dk/t analysis. Age, however, was a significant (inverse) predictor of the rate of corneal deswelling, only in lens-wearing eyes. Conclusions: A large proportion of variability in corneal swelling is because of subject-specific differences in corneal response to hypoxia. This shows that "low swellers" and "high swellers" actually do exist.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".