Corneal Neurotization: Preoperative Patient Workup and Surgical Decision-making
Bibliographic record
Abstract
Background: The use of sensory nerve transfers to the anesthetic cornea has transformed the treatment of neurotrophic keratopathy by restoring ocular surface sensation and activating dysfunctional epithelial repair mechanisms. However, despite numerous reports on surgical techniques, there is a scarcity of information on the interdisciplinary management, preoperative assessment, and surgical decision-making, which are equally critical to treatment success. Methods: This Special Topic presents a standardized, interdisciplinary preoperative workup based on our 10-year experience with corneal neurotization in 32 eyes of patients with neurotrophic keratopathy. Results: Our assessment includes a medical history review, ophthalmic evaluation, and systematic facial sensory donor nerve mapping for light touch and pain modalities. This approach enables evidence-based patient selection, optimal surgery timing, and suitable donor nerve identification, including backup options. Conclusions: Based on a decade-long experience, this special topic highlights the importance of interdisciplinary collaboration and provides a practical roadmap for optimizing patient selection and surgical decision-making in patients undergoing corneal neurotization.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".