Corneal, Limbal, and Conjunctival Epithelial Thickness from Optical Coherence Tomography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 it