Effects of Scleral‐lens Tear Clearance on Corneal Edema and Post‐lens Tear Dynamics: A Pilot Study
Bibliographic record
Abstract
SIGNIFICANCE: The present study with small-diameter scleral lenses (SLs) revealed that post-lens tear thickness (PoLTT) was significantly associated with post-lens tear mixing, but not with central corneal edema, after short-term SL wear. PURPOSE: The aim of this study was to investigate the influence of SL tear clearance (PoLTT) on central corneal thickness and post-lens tear dynamics during 5-hour lens wear. METHODS: Neophytes with no active ocular disease were fitted bilaterally with SLs (hofocon A; 15.6-mm diameter; ~438-μm thickness; 97 Dk; 1.44 refractive index) with various initial PoLTT values ranging from 74 to 543 μm. Central corneal thickness and PoLTT were measured using optical coherence tomography during lens wear. Tear mixing was assessed using fluorogram and "out-in" method. RESULTS: The mean central corneal edema after 5-hour lens wear was 1.51% (95% confidence interval, 1.26 to 1.76%; P < .001), reached its peak at 2-hour post-lens insertion (1.65% [95% confidence interval, 1.45 to 1.85%]), and was independent of PoLTT. The fastest fluorescence decay of the post-lens tear film was observed superiorly. The fluorescence decay rate increased from center to periphery in all quadrants except superiorly. An inverse relationship was found between PoLTT and fluorescence decay rate at both 20-minute and 5-hour wear after lens insertion (P < .05). Excluding observations with out-in time exceeding 5 minutes, we found a direct relationship between PoLTT at 20 minutes after lens insertion and out-in time (P = .047). The % change in the PoLTT after 5-hour wear was greater with a thinner initial tear clearance than those with a thicker one (P = .034). CONCLUSIONS: Within our study parameters, a thinner PoLTT under a small-diameter SL was associated with faster PoLTT mixing. However, there was no relationship between PoLTT and central corneal thickness during 5-hour SL wear.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".