Bibliographic record
Abstract
PURPOSE OF REVIEW: Neurotrophic keratopathy is a devastating corneal condition that can lead to ocular morbidity and blindness. Current medical and surgical treatments poorly tackle the essential problem of corneal aesthesia and hence fail to provide a permanent cure. Recent advances in corneal neurotization techniques have shown promise to restore corneal nerves in neurotrophic keratopathy. This article aims at reviewing the current surgical advances, along with the current thoughts and evidence available for corneal nerve regeneration. RECENT FINDINGS: Corneal neurotization was first introduced in 2009 by Terzis et al., but recently picked up more interest since 2014. Direct and indirect neurotization are being developed, and different nerves (sural nerve, great auricular nerve) have been explored for interposition between frontal nerve branches and the cornea. New endoscopic techniques are introduced for less invasive approaches. On the corneal front, confocal microscopy and esthesiometry studies have established that the regeneration of the corneal nerves is happening 6 months after the procedure. SUMMARY: Neurotization is a budding revolutionary technique that shows promise of cure for neurotrophic corneas, but at this stage, it is still reasonably invasive and still reserved for selected patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".