Clinical Evaluation of Ripasudil for Corneal Edema: A Large-Scale Retrospective Cohort Study
Bibliographic record
Abstract
Objectives: This study evaluated the therapeutic potential of topical Ripasudil hydrochloride hydrate in managing various forms of corneal edema. Methods: This retrospective study included 96 patients of 72.20 ± 10.52 years, with 53 females (55.2%) who were treated with Ripasudil for corneal edema, with a mean treatment duration of 5.2 ± 2.3 months, divided into four groups: post-cataract surgery (n = 32), Fuchs endothelial corneal dystrophy (FECD; n = 29), post-Descemet membrane endothelial keratoplasty (DMEK; n = 25), and post-penetrating keratoplasty (PKP; n = 10). All patients were treated with Ripasudil, typically administered three times daily in the first week and twice daily in the following months. Clinical efficacy outcomes were assessed using changes in best-corrected visual acuity (BCVA), central corneal thickness (CCT), and endothelial cell count (ECC) with specular microscopy, anterior segment optical coherence tomography (OCT), and slit-lamp examination, while intraocular pressure (IOP) was measured using the iCare tonometer. Results: Ripasudil treatment led to a reduction in CCT and improvement in visual acuity across most groups, with minimal changes in ECC. CCT decreased by 30.44 μm (p < 0.001), 25.56 μm (p < 0.001), 8.41 μm (p = 0.05), and 6.80 μm (p > 0.1); visual acuity improved by 0.27 (p = 0.001), 0.18 (p = 0.02), 0.17 (p = 0.025), and 0.07 logMAR units (p > 0.1); and ECC changed by +7.0 (p > 0.1), 15.4 (p > 0.1), −7.6 (p > 0.1), and 2.3 cells/mm2 (p > 0.1) in the post-cataract surgery, FECD, post-DMEK, and post-PKP groups, respectively. Conclusions: No adverse events or progression of edema were recorded during the follow-up period. These findings support the role of Ripasudil as a non-invasive pharmacological approach to managing corneal edema and delaying or possibly avoiding surgical interventions, such as corneal transplantation, in selected cases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".