Smile Reconstruction in Adults with Free Muscle Transfer Innervated by the Masseter Motor Nerve: Effectiveness and Cerebral Adaptation
Bibliographic record
Abstract
BACKGROUND: This study assesses the ability of the masseter motor nerve-innervated microneurovascular muscle transfer to produce an effective smile in adult patients with bilateral and unilateral facial paralysis. METHODS: The operation consists of a one-stage microneurovascular transfer of a portion of the gracilis muscle that is innervated with the masseter motor nerve. The muscle is inserted into the cheek and attached to the mouth to produce a smile. The outcomes assessed were the amount of movement of the transferred muscle; the aesthetic quality of the smile; the control, use, and spontaneity of the smile; and the functional effects on eating, drinking, and speech. The study included 27 patients aged 16 to 61 years who received 45 muscle transfers. RESULTS: All 45 muscle transfers developed movement. The commissure movement averaged 13.0 +/- 4.7 mm at an angle of 47 +/- 15 degrees above the horizontal, and the mid upper lip movement averaged 8.3 +/- 3.0 mm at 42 +/- 17 degrees. Age did not affect the amount of movement. Patients older than 50 years had the same amount of movement as patients younger than 26 years (p = 0.605). Ninety-six percent of patients were satisfied with their smile. CONCLUSIONS: A spontaneous smile, the ability to smile without thinking about it, occurred routinely in 59 percent and occasionally in 29 percent of patients. Eighty-five percent of patients learned to smile without biting. Age did not affect the degree of spontaneity of smiling or the patient's ability to smile without biting.
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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) | 0.000 | 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) | 0.002 | 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".