The Impact of Scleral Contact Lens Vault on Visual Acuity and Comfort
Bibliographic record
Abstract
PURPOSE: To assess how varying degrees of corneal clearance of scleral contact lenses (ScCL) impact visual acuity (VA) and comfort in patients with corneal ectasia. METHOD: Three ScCL were fitted to 20 subjects with previous diagnosis of either keratoconus (n=18) or pellucid marginal degeneration (n=2). Fitting of ScCL was based on corneal sagittal height (CSH) measured with Visante OCT at a 15-mm chord on the horizontal meridian. To select the ScCL from the diagnostic lens set, values of 325, 375, and 425 μm were randomly added in sequence to CSH. Subjects wore ScCL for 1 hr. Central corneal clearance (CCC) and topographic corneal clearance (TCC) along the vertical meridian were assessed using an ultralong optical coherence tomographer. High-contrast VA (HCVA) and low-contrast VA (LCVA) were measured using a logarithm of the minimum angle of resolution VA chart, and comfort ratings were obtained with a standard comfort scale (0-100). RESULTS: Mean CSH in the horizontal meridian was 3.78±0.53 (range: 3.33-4.17) mm at a 15-mm chord. Mean CCC was 190±100 μm (TCC: 160±94 μm at +3 mm and 180±94 μm at -3 mm), 360±120 μm (TCC: 260±100 μm at +3 mm and 330±110 μm at -3 mm), and 450±170 μm (TCC: 320±120 μm at +3 mm and 400±120 μm at -3 mm) for each lens (P=0.001). Mean HCVA for lenses 1, 2, and 3 were 0.05±0.12, 0.07±0.11, and 0.11±0.08 respectively, which were significantly different (P=0.02). Tukey post hoc analysis showed that this difference was only significant between lenses 1 and 3 (P=0.01). Similar findings were found for LCVA. Comfort ratings for lenses 1, 2, and 3 were 74.9±9.2, 79.7±11.6, and 78.6±10.8, respectively (P=0.24). CONCLUSION: The CSH is an effective method of determining the appropriate lens/cornea relationship. Lens 2 (+375 μm) gave the best combination of acuity and comfort ratings. Evaluation of the fluorescein pattern must be balanced with VA and comfort ratings for successful fitting in a clinical setting.
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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 | 0.002 |
| 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) | 0.003 | 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".