Optical Coherence Tomography Anatomy of the Corneal Endothelial Transplantation Wound
Bibliographic record
Abstract
PURPOSE: The goal of this study was to prospectively assess the deep lamellar endothelial keratoplasty (DLEK) wound anatomy and its evolution during the 12 months after surgery, using optical coherence tomography (OCT). METHODS: The eyes of 8 patients (1 eye per patient) who consecutively underwent DLEK for Fuchs dystrophy or pseudophakic bullous keratopathy were prospectively studied before and 1, 3, 6, and 12 months after surgery. The Stratus OCT apparatus (Carl Zeiss Meditec, Dublin, CA) was used to acquire central and radial scans perpendicular to the wound at 3-, 6-, 9-, and 12-o'clock positions. The following parameters were analyzed: central total thickness, posterior donor-recipient edges gap, donor-recipient height mismatch, tissue compression, and graft detachment. RESULTS: A posterior gap was observed in 4 of the 8 DLEK eyes. At 12 months, the mean gap contour, depth, and width were 242 +/- 67, 101 +/- 45, and 87 +/- 29 microm, respectively. A step was documented in all DLEK eyes (average step height 108 +/- 24 microm). A micrograft detachment was observed in one case and tissue compression in another. In all corneas, the mean central corneal thickness returned to normal range and almost normal anatomy with time after surgery. CONCLUSIONS: OCT was found to be a very useful tool for DLEK corneal wound architecture analysis. It revealed microscopic wound irregularities and allowed their quantitative follow-up with time.
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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".