Recovery from hyperemia after overnight wear of low and high transmissibility hydrogel lenses
Bibliographic record
Abstract
PURPOSE: To measure the limbal vascular response after 8 hours of eye closure while wearing high and low permeability lenses compared to control eyes without lenses. METHOD: Twenty neophyte participants wore lotrafilcon A silicone hydrogel lenses (HDk; Dk = 140) or etafilcon A hydrogel lenses (LDk; Dk = 18). On two different nights the lenses were randomly worn for 8 hours during sleep in the right eyes only. Left eyes were non-lens wearing controls. Biomicroscopic images of the temporal limbal area were videotaped at baseline, on eye opening and every 20 minutes for 3 hours. A masked observer graded digitized images of the limbal area. RESULTS: On waking and after lens removal there were no differences in hyperemia between the HDk and LDk lens wearing eyes. There were also no differences at any time between the HDk lens wearing eyes and their control eyes (p > 0.05). On waking the eyes wearing the LDk lens were more hyperemic compared to baseline (p < 0.001) and compared to their control eyes at 20 (p < 0.001) and 180 minutes (p = 0.01), indicating slower recovery from hyperemia. The HDk lens wearing eyes recovered to their baseline levels by 180 minutes (p = 0.99), compared to the LDk lens wearing eyes, which had not recovered to baseline levels by 180 minutes (p = 0.04). CONCLUSION: The reduction in hyperemia over time of the HDk lens wearing eyes was the same as the controls. The LDk lens wearing eyes were more hyperemic than the controls on waking and the reduction in hyperemia over time was slower. This suggests that the slower recovery from hyperemia may be affected by the lower oxygen transmissibility of the LDk lens.
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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) | 0.002 | 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".