Cross-Face Nerve Grafting with Infraorbital Nerve Pathway Protection: Anatomic and Histomorphometric Feasibility Study
Bibliographic record
Abstract
Smiling is an important aspect of emotional expression and social interaction, leaving facial palsy patients with impaired social functioning and decreased overall quality of life. Although there are several techniques available for facial reanimation, staged facial reanimation using donor nerve branches from the contralateral, functioning facial nerve connected to a cross-face nerve graft (CFNG) is the only technique that can reliably reproduce an emotionally spontaneous smile. Although CFNGs provide spontaneity, they typically produce less smile excursion than when the subsequent free functioning muscle flap is innervated with the motor nerve to the masseter muscle. This may be explained in part by the larger number of donor motor axons when using the masseter nerve, as studies have shown that only 20% to 50% of facial nerve donor axons successfully cross the nerve graft to innervate their targets. As demonstrated in our animal studies, increasing the number of donor axons that grow into and traverse the CFNG to innervate the free muscle transfer increases muscle movement, and this phenomenon may provide patients with the benefit of improved smile excursion. We have previously shown in animal studies that sensory nerves, when coapted to a nerve graft, improve axonal growth through the nerve graft and improve muscle excursion. Here, we describe the feasibility of and our experience in translating these results clinically by coapting the distal portion of the CFNG to branches of the infraorbital nerve.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".