Comparison of Human Central Cornea and Limbus <i>in Vivo</i> Using Optical Coherence Tomography
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to compare central corneal and limbal total and epithelial thickness using a commercially available optical coherence tomographer. METHODS: A Humphrey-Zeiss Optical Coherence Tomographer (OCT [Carl Zeiss, Meditec, Dublin, CA]) was used to obtain corneal images from 10 subjects. Central corneal and limbal total and epithelial thickness of both eyes were measured using the OCT. Each OCT image comprised 100 measurements, 10 nasal, 10 central, and 10 temporal measurements from each image were analysed. RESULTS: The central corneal and epithelial thickness of the right and the left eyes were 507.9 +/- 35.8 microm, 58.4 +/- 2.5 microm, 506.9 +/- 37.4 microm, and 58.5 +/- 2.5 microm, respectively. There were no differences between eyes (p > 0.05). The nasal and temporal limbal total and epithelial thickness of the right and left eyes were 703.8 +/- 32.1 microm, 704.9 +/- 31.0 microm, 76.8 +/- 3.5 microm, 77.9 +/- 2.9 microm, 704.4 +/- 31.8 microm, 706.3 +/- 32.5 microm, 77.5 +/- 2.8 microm, and 77.8 +/- 2.5 microm, respectively. There were no differences between the nasal and temporal total and epithelial thickness of both eyes (p > 0.05). However, there was a statistical difference between the central corneal and limbal total and epithelial thickness (both p < 0.05). CONCLUSIONS: Central cornea and limbus are measurably different using OCT. Central cornea is thinner than limbus for both total thickness and epithelial thickness. There is no difference between eyes of central corneal and limbal total and epithelial thickness. Optical Coherence Tomography is a useful instrument for in vivo human limbal morphometry.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".