Thickness Mapping of the Cornea and Epithelium Using Optical Coherence Tomography
Bibliographic record
Abstract
PURPOSE: To measure corneal and epithelial thickness across four meridians using Optical Coherence Tomography (OCT) and to compare these measurements between normal non-lens wearers (NLW), rigid gas permeable (RGP) lens wearers, and RGP-wearing keratoconics (KC). METHODS: Both eyes of 60 subjects were measured (20 NLW, nine female:11 male, 27.6 +/- 5.9 years; 20 RGP, 20 female, 23.9 +/- 7.6 years; and 20 KC, seven female:13 male, 32.4 +/- 8.1 years). A customized fixation target employing LEDs in eight directions of gaze was attached to the OCT and corneal images obtained. Raw OCT scans were analyzed to yield values for corneal and epithelial thickness and color-coded maps were compiled. RESULTS: Central corneal thickness (CCT) was thinnest in KC (447 +/- 68 microm) and similar between RGP (518 +/- 32 microm; pKC < 0.001) and NLW (517 +/- 21 microm) (p(KC) < 0.001 NLW pRGP > 0.05). Peripheral corneal thickness in NLW was thickest in the superior temporal and thinnest in the inferior (I) regions (superior temporal(thickest) vs. I(thinnest) p < 0.001). Central epithelial thickness was thinnest in KC (44 +/- 7 microm), followed by RGP (50 +/- 4 microm), then NLW (54 +/- 2 microm) (pKC < 0.001 NLW p(RGP) < 0.05). Central epithelial thickness in the KC group was significantly thinner than in the RGP group (p < 0.001). In the NLW group, peripheral epithelial thickness was thicker (63 +/- 5 microm) than central (p < 0.001) and was thickest in the superior (S) region and thinnest in the inferior (I) region (S(thickest) vs. I(thinnest) p < 0.001). KC epithelium was thinnest in the inferior temporal meridian (42 +/- 5 microm). CONCLUSIONS: Thickness of the normal cornea and epithelium was greatest in the superior region. In all groups, the inferior cornea and epithelium was thinnest, and to a greater extent in the KC group.
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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 | 0.001 | 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) | 0.001 | 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".