Fellow Eye Comparison of Corneal Thickness and Curvature in Descemet Membrane Endothelial Keratoplasty and Descemet Stripping Automated Endothelial Keratoplasty
Bibliographic record
Abstract
PURPOSE: To compare posterior corneal curvature in the fellow eye of the same patients after Descemet membrane endothelial keratoplasty (DMEK) and Descemet stripping automated endothelial keratoplasty (DSAEK). METHODS: This retrospective, case series comparative study included consecutive patients who underwent DSAEK in one eye and DMEK in the fellow eye. Each eye underwent corneal evaluation with Pentacam HR (Oculus, Wetzlar, Germany). Postoperative corneal curvature, corneal thickness, and visual acuity were assessed. RESULTS: Twenty eyes of 10 patients (5 women and 5 men) aged 72.5 ± 13.5 (range, 42-87) years were included. No significant differences were observed between front flat K's (43.01 ± 1.6 vs. 43.5 ± 0.9, P = 0.27) and front steep K's (44.17 ± 1.5 vs. 44.52 ± 0.7, P = 0.39) in DMEK vs. DSAEK eyes, accordingly. Posterior curvature was statistically significantly flatter in DMEK compared with DSAEK eyes; back flat K's (-6.30 ± 0.2 vs. -6.84 ± 0.6, P = 0.012), back steep K's (-6.64 ± 0.1 vs. -7.2 ± 0.3, P = 0.03), and back Km (-6.45 ± 0.1 vs. -6.99 ± 0.4, P = 0.005), accordingly. Corneas in DMEK eyes were significantly thinner than in DSAEK eyes (541.0 ± 61 vs. 627.9 ± 70 μm, P = 0.007). CONCLUSIONS: Eyes that underwent DSAEK surgery have thicker corneas with steeper posterior corneal curvature than fellow eyes that underwent DMEK. This difference may explain the hyperopic shift commonly observed after DSAEK and should be considered when choosing an intraocular lens for cataract surgery.
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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 | 0.003 |
| 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) | 0.002 | 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".