Corneal, Limbal, and Conjunctival Epithelial Thickness from Optical Coherence Tomography
Bibliographic record
Abstract
PURPOSE: To compare human central corneal, limbal and bulbar conjunctival epithelial thickness in vivo using an Optical Coherence Tomographer (OCT). METHODS: Thirteen healthy human subjects participated in this study. An OCT (Carl Zeiss, Meditec, Dublin, CA) was used to image central cornea, temporal corneo-scleral limbus and bulbar conjunctiva of the left eye. Two images were taken at each location. Thirty central measurements were averaged from each image for quantifying epithelial thickness. RESULTS: In addition to the central cornea and limbal region, a band corresponding to bulbar conjunctival "epithelium" is apparent in OCT images, with respective thicknesses of 54.7 +/- 1.9 microm (mean +/- SD), 79.6 +/- 7.4 microm and 44.9 +/- 3.4 microm that are statistically significant different (repeated measures analysis of variance p < 0.01, post hoc test shows all p < 0.01). CONCLUSIONS: This study demonstrates that it is possible to image the epithelial tissue in humans in vivo using optical coherence tomography, and in these subjects, the corneo-limbal epithelium is the thickest, while the bulbar conjunctival epithelium is the thinnest and the corneal epithelium has intermediate thickness.
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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 | 0.000 | 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".