Metrics of the normal cornea: anterior segment imaging with the Visante OCT
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to obtain anterior segment biometry for 40 normal eyes and to measure variables that may be useful to design large diameter gas permeable contact lenses that sit outside the region normally viewed by corneal topographers. Also, the distribution of these variables in the normal eye and how well they correlated to each other were determined. METHODS: This is a cross-sectional study, in which data were collected at a single study visit. Corneal topography and imaging of the anterior segment of the eye were performed using the Orbscan II and Visante OCT. The variables that were collected were horizontal K reading, central corneal/scleral sagittal depth at 15 mm chord, and nasal and temporal angles at the 15 mm chord using the built-in software measurement tools. RESULTS: The central horizontal K readings for the 40 eyes were 43 +/- 1.73 D (7.85 +/- 0.31 mm), with +/- 95% confidence interval (CI) of 38.7 (8.7 mm) and 46.6 D (7.24 mm). The mean corneal/scleral sagittal depth at the 15 mm chord was 3.74 +/- 0.19 mm and the range was 3.14 to 4.04 mm. The average nasal angle (which was not different from the temporal angle) at the 15 mm chord was 39.32 +/- 3.07 degrees and the +/- 95%CI was 33.7 and 45.5 degrees. The correlation coefficient comparing the K reading and the corneal/scleral sagittal depth showed the best correlation (0.58, p < 0.001). The corneal/scleral sagittal depth at 15 mm correlated less with the nasal angle (0.44, p = 0.004) and the weakest correlation was for the nasal angle at 15 mm with the horizontal readings (0.32, p = 0.046). CONCLUSION: The Visante OCT is a valuable tool for imaging the anterior segment of the eye. The Visante OCT is especially effective in providing the biometry of the peripheral cornea and sclera and may help in fitting GP lenses with a higher percentage of initial lens success, when the corneal sag and lens sag are better matched.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".