Masseter nerve transfer with dual function for smile and eye closure
Bibliographic record
Abstract
In patients with facial nerve palsy, the masseter nerve (MN) transfer may be used to improve orbicularis oculi (OO) resting tone and eye closure. This study evaluated OO muscle function after "selective" MN transfer compared to upper cross face nerve graft (CFNG) in patients with facial nerve palsy. This retrospective study included adults with facial nerve palsy who underwent CFNG and selective MN transfer for dual reinnervation of OO and zygomaticus major. Outcomes included electromyography evidence of OO reinnervation, clinical assessment, Sunnybrook Facial Grading System to assess symmetry, synkinesis, resting and dynamic eyelid position, and eMotrics for eye closure assessment. The study included 16 patients (mean age at surgery: 53 years; mean time from injury to surgery: 6.7 months; mean follow-up, clinical: 29.4 months and video: 20.6 months). Postoperatively, there were significant improvements in resting symmetry for the cheek (p<0.001), and symmetry of voluntary gentle eye closure (p=0.004) and smile (p<0.0001). Postoperative synkinesis was observed with gentle eye closure and open mouth smile (mean score 0.44). Postoperative electromyography revealed innervation of OO via MN transfer in all patients (n=13) and CFNG in 5 patients. Postoperative eyelid position was significantly different at the marginal reflex distance with MN transfer activation (p<0.01). Selective MN transfer is effective in facilitating eye closure via functional innervation of the OO with the activation of the nerve transfer. Owing to the importance of eye closure for corneal protection, the selective MN transfer should be considered in patients undergoing surgery for facial reanimation.
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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 | 0.000 |
| 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".