Scleral Lens Clearance Assessment with Biomicroscopy and Anterior Segment Optical Coherence Tomography
Bibliographic record
Abstract
SIGNIFICANCE: It is important to be able to accurately estimate the central corneal clearance when fitting scleral contact lenses. Tools available have intrinsic biases due to the angle of viewing, and therefore an idea of the amount of error in estimation will benefit the fitter. PURPOSE: To compare the accuracy of observers' ability to estimate scleral contact lens central corneal clearance (CCC) with biomicroscopy to measurements using slit-lamp imaging and anterior segment optical coherence tomography (AS-OCT). METHODS: In a Web-based survey with images of four scleral lens fits obtained with a slit-lamp video imaging system, participants were asked to estimate the CCC. Responses were compared with known values of CCC of these images determined with an image-processing program (digital CCC) and using the AS-OCT (AS-OCT CCC). Bland-Altman plots and concordance correlation coefficients were used to assess the agreement of CCC measured by the various methods. RESULTS: Sixty-six participants were categorized for analysis based on the amount of experience with scleral lens fitting into novice, intermediate, or advanced fitters. Comparing the estimated CCC to the digital CCC, all three groups overestimated by an average of +27.3 ± 67.3 μm. The estimated CCC was highly correlated to the digital CCC (0.79, 0.92, and 0.94 for each group, respectively). Compared with the CCC measurements using AS-OCT, the three groups of participants overestimated by +103.3 μm and had high correlations (0.79, 0.93, and 0.94 for each group). DISCUSSION: Results from this study validate the ability of contact lens practitioners to observe and estimate the CCC in scleral lens fittings through the use of biomicroscopic viewing. Increasing experience with scleral lens fitting does not improve the correlation with measured CCC from digital or the AS-OCT. However, the intermediate and advanced groups display significantly less inter-observer variability compared with the novice group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".