Efficacy of Corneal Neurotization Surgery in Eyes With Neurotrophic Keratopathy
Bibliographic record
Abstract
PURPOSE: Corneal neurotization (CN) restores innervation to anesthetic corneas, often caused by neurotrophic keratopathy (NK), using healthy donor nerves or grafts. Despite promising outcomes, its efficacy remains unclear. We conducted a meta-analysis to assess the efficacy of CN in eyes with NK and its influence on postoperative outcomes. METHODS: We systematically searched PubMed, EMBASE, and Cochrane. We calculated mean differences (MDs) with 95% confidence intervals (CIs) for continuous outcomes, following PRISMA guidelines. We used the R software for statistical analysis. RESULTS: We included 17 studies (232 eyes; mean follow-up: 19.07 ± 7.45 months). CN improved best-corrected visual acuity (MD: -0.44; 95% CI: -0.66 to -0.21; P < 0.01; I2 = 88.7%), CS (MD: 32.42 mm; 95% CI: 27.45 to 37.38; P < 0.001), corneal nerve fiber density (MD: 12.15 n/mm 2 ; 95% CI: 5.09 to 19.20; P < 0.001), corneal nerve branch density (MD: 18.88 n/mm 2 ; 95% CI: 8.89 to 28.87; P < 0.001), and corneal nerve fiber length (MD: 9.40 mm/mm 2 ; 95% CI: 6.31 to 12.50; P < 0.001) and corneal nerve total branch density (MD: 25.48 n/mm 2 ; 95% CI: 14.09 to 36.87; P < 0.001). CN also reduced Mackie scores (MD: -1.20; 95% CI: -1.42 to -0.99; P < 0.001). CONCLUSIONS: CN enhances both corneal function and innervation. These findings support CN as a promising and effective approach for restoring corneal function in NK.
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How this classification was reachedexpand
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".