Treatment of neurotrophic keratopathy with minimally invasive corneal neurotisation: long-term clinical outcomes and evidence of corneal reinnervation
Bibliographic record
Abstract
AIM: To report clinical outcomes and evidence of corneal innervation in patients with neurotrophic keratopathy (NK) treated with minimally invasive corneal neurotisation (MICN) using a sural nerve graft and donor sensory nerves from the face. METHODS: Patients undergoing MICN at The Hospital for Sick Children, Toronto, Canada were prospectively recruited. Data on central corneal sensation (CCS, measured with Cochet-Bonnet aesthesiometer), best-corrected visual acuity (BCVA) and corneal epithelial integrity were collected. In four patients who subsequently underwent keratoplasty, immunohistochemical analysis was performed on the corneal explants. One patient underwent magnetoencephalography (MEG) after MICN to characterise the neurophysiological pathways involved. RESULTS: Between November 2012 and February 2017, 19 eyes of 16 patients underwent MICN. Mean follow-up was 24.0±16.1 months (range, 6-53). Mean CCS significantly improved from 0.8±2.5 mm to 49.7±15.5 mm at final follow-up (p<0.001). Mean BCVA remained stable, and the number of episodes of corneal epithelial defects after MICN was significantly reduced compared with the year leading up to the procedure (21% vs 89%, respectively; p<0.0001). In the four eyes that underwent keratoplasties after MICN, all transplants fully re-epithelialised and regained sensation subsequently. Immunohistochemistry of the corneal explants demonstrated evidence of corneal reinnervation. In one patient who was 8 months after MICN, novel neuroactivity was detected on MEG in the ipsilateral somatosensory cortex on mechanical stimulation of the reinnervated cornea. CONCLUSIONS: By providing an alternative source of innervation, MICN improves corneal sensation and stabilises the corneal epithelium, permitting optical keratoplasty for patients with NK-related corneal opacity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".