Fellow Eye Comparison of Corneal Thickness and Curvature in Descemet Membrane Endothelial Keratoplasty and Descemet Stripping Automated Endothelial Keratoplasty
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 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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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