Surgical Management of Fuchs Endothelial Corneal Dystrophy: A Treatment Algorithm and Individual Patient Meta-Analysis of Descemet Stripping Only
Bibliographic record
Abstract
PURPOSE: This study aims to determine predictive factors for success of Descemet stripping only (DSO) in Fuchs corneal endothelial dystrophy and propose a DSO treatment algorithm. METHODS: Ovid MEDLINE, Embase, and Cochrane CENTRAL databases were searched to evaluate DSO case series, including combined phacoemulsification and DSO, and the use of Rho-kinase inhibitors (ROC-i). Our primary outcome was success of corneal clearance. Secondary outcomes included the time to corneal clearance, the postoperative endothelial cell count (ECC), and the impact of ROC-i. RESULTS: Sixty-eight cases were evaluated with a mean follow-up of 12.4 months. DSO corneal clearance was achieved in 85% (n = 58) with a mean time of 4.9 weeks for the ROC-i group compared with 10.1 weeks in the observation group (P < 0.0001). The mean central ECC postoperatively was higher in the ROC-i group compared with the observation group 1151 ± 245 versus 765 ± 169 cells/mm2, respectively (P < 0.018). The postoperative best-corrected visual acuity (BCVA) improved in 61 eyes (90%), with mean final BCVA of 0.17 (0.26) logMAR (P = 0.001), which was statistically significant compared with preoperative BCVA. Factors influencing success were 4-mm descemetorhexis size, a clear peripheral ECC with no clinical sequelae of decompensation or guttae, and a low central corneal thickness. No intraoperative complications were noted. The commonest postoperative complication was deep corneal stromal scars noted at the descemetorhexis edge (n = 9). CONCLUSIONS: DSO has a role in the treatment of a subset of patients with Fuchs corneal endothelial dystrophy and that adjuvant treatment with ROC-i may lead to faster corneal clearance.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.024 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".