Epithelial Thickness Changes from the Induction of Myopia with CRTH RGP Contact Lenses
Bibliographic record
Abstract
PURPOSE: To investigate changes in epithelial thickness after overnight wear of CRTH rigid gas-permeable (RGP) lenses (Paragon Vision Sciences, Mesa, AZ) for the correction of hyperopia. METHODS: Twenty subjects wore a +3.50 D hyperopia-correcting CRTH lens on one eye for a single night in an attempt to induce myopia (first study). The untreated eye served as the control. Corneal and epithelial thickness was measured at nine points across the horizontal meridian by OCT. Measurements were obtained the night before lens wear, immediately after lens removal the next morning, and 1, 3, 6, and 12 hours after removal. Measurements were obtained 28 hours later, to observe recovery. Then, the attempted hyperopic corrections of +1.50 and +3.50 D were evaluated, using CRTH lenses in both eyes of 20 subjects for a single night (second study). RESULTS: All values were compared to baseline unless otherwise stated. In the first study, the treated eye's central and midperipheral epithelial thickness increased by 21.5% +/- 8.6% and 13.3% +/- 7.6%, respectively, after lens removal (P < 0.001). The control eye's central epithelial thickness (CET) increased by 7.1% +/- 6.0% (P < 0.05). In the second study, CET increased by 17.6% +/- 8.5% (P < 0.001) in the +3.50 D-treated eye and by 13.3% +/- 4.8% (P < 0.001) in the +1.50 D-treated eye. Midperipheral epithelial thickening was 5.9% +/- 4.7% (P < 0.05) in the +3.50 D-treated eye and 6.0% +/- 6.3% (P < 0.05) in the +1.50 D-treated eye. CONCLUSIONS: CRTH lenses, designed to correct hyperopia, when worn overnight, caused an increase in CET. The amount of epithelial change seemed to differ with modified lens design.
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 | 0.000 | 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".