The Degree of Facial Movement following Microvascular Muscle Transfer in Pediatric Facial Reanimation Depends on Donor Motor Nerve Axonal Density
Bibliographic record
Abstract
BACKGROUND: Free functional muscle transfer to the face is a standard of facial animation. The contralateral facial nerve, via a cross-face nerve graft, provides spontaneous innervation for the transferred muscle, but is not universally available and has additional shortcomings. The motor nerve to the masseter provides an alternative innervation source. In this study, the authors compared donor nerve histomorphometry and clinical outcomes in a single patient population undergoing free muscle transfer to the face. METHODS: Pediatric patients undergoing dynamic facial (re-)animation with intraoperative nerve biopsies and gracilis transfer to the face powered by either the contralateral facial nerve via a cross-face nerve graft or the motor nerve to the masseter were reviewed over a 7-year period. Myelinated nerve counts were assessed histomorphometrically, and functional outcomes were evaluated with the Scaled Measurement of Improvement in Lip Excursion software. RESULTS: From 2004 to 2011, 91 facial (re-)animation procedures satisfied study inclusion criteria. Average myelinated fiber counts were 6757 per mm2 in the donor facial nerve branch, 1647 per mm in the downstream cross-face nerve graft at the second stage, and 5289 per mm in the masseteric nerve. Reconstructions with either innervation source resulted in improvements in oral commissure excursion and smile symmetry, with the greatest amounts of oral commissure excursion noted in the masseteric nerve group. CONCLUSIONS: Facial (re-)animation procedures with use of the cross-face nerve graft or masseteric nerve are effective and result in symmetric smiles. The masseteric nerve provides a more robust innervation source and results in greater commissure excursion. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, III.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".