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Record W4410578078 · doi:10.1016/j.otot.2025.05.002

A Novel Technique for Lower Lip Depressor Re-Animation Using the Nerve to Myloyhoid to Marginal Mandibular Nerve Transfer

2025· article· en· W4410578078 on OpenAlexaff
Robert Calvisi, C J Li, Marc Levin, Antoine Eskander, Danny Enepekides, Kevin Higgins

Bibliographic record

VenueOperative Techniques in Otolaryngology-Head and Neck Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicFacial Nerve Paralysis Treatment and Research
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMandibular nerveAnimationAnatomyMedicineOrthodonticsComputer scienceComputer graphics (images)Molar

Abstract

fetched live from OpenAlex

Introduction Reanimation of the paralyzed face remains a challenging area for the reconstructive surgeon, with several static and dynamic surgical options. This study aimed to describe a novel technique for lower lip depressor re-animation using nerve to mylohyoid (MHN) to marginal mandibular nerve (MMN) transfer. Methods Four patients underwent MHN-MMN transfer following facial nerve sacrifice following oncological resection of salivary gland malignancy or MMN sacrifice during neck dissection for oral cavity malignancy. Results All four patients recovered from their surgeries without perioperative complications. Re-innervation is not yet seen given the short duration of follow-up time. The anatomical feasibility of transferring the MHN to the MMN for re-innervation of the lower lip depressors is a feasible surgical reanimation technique with anatomical compatibility and minimal donor-site morbidity. Conclusion The MHN to MMN nerve transfer is a technically feasible lower lip depressor reanimation technique with theoretical advantages over currently accepted treatment options. Increased patient numbers and longer follow-up intervals are required to study the clinical effectiveness.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.960

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.363
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueOperative Techniques in Otolaryngology-Head and Neck SurgerySame topicFacial Nerve Paralysis Treatment and ResearchFrench-language works237,207