Repeatability and comparative study of corneal thickness using the Visante™ OCT, OCT II and Orbscan II™
Bibliographic record
Abstract
The first purpose of this study was to measure the repeatability the VisanteTM Optical coherence tomographer (OCT) in a normal sample. The second was to compare corneal thickness measured with the VisanteTM OCT to the Zeiss-Humphrey OCT II (model II, Carl Zeiss Meditec) adapted for anterior segment imaging and to the Orbscan IITM (Bausch and Lomb). Fifteen healthy participants were recruited. At the Day 1 visit, the epithelial and total corneal thickness across the central 10 mm of the horizontal meridian was measured using the OCT II and the VisanteTM OCT. Only total corneal thickness across the central 10 mm of the horizontal meridian was measured using the Orbscan II. These measurements were repeated on Day 2. Mean central corneal and epithelial thickness using the Visante™ OCT at the apex of the cornea was 536±27 mm and 55±2.3 mm. Mean corneal and epithelial thickness using OCT II at the apex was 520±25 mm and 56±4.9 mm. Mean total corneal thickness measured with the Orbscan II was 609±29 mm. The coefficient of repeatability (COR) ranged from ±7.71 to ±8.98 mm for total corneal thickness and from ±8.72 to ±9.92 mm for epithelial thickness. Correlation coefficients of concordance (CCC’s) were high for total corneal thickness for test-retest differences ranging from 0.97 to 0.99, CCCs for epithelial thickness showed moderate concordance for both the instruments. There is good repeatability of corneal and epithelial thickness using each OCT for test-retest differences compared to the between instrument repeatability. Measurements of epithelial thickness were less robust.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".