Periocular motor neurotization: a systematic review of techniques and outcomes in orbicularis oculi reinnervation
Bibliographic record
Abstract
Background: Periocular motor neurotization is a surgical approach that can be indicated for the restoration of dynamic eyelid function in patients with facial nerve palsy. The orbicularis oculi muscle, responsible for eyelid closure, can be the target of a variety of re-innervation techniques, such as direct nerve transfers and muscle transpositions. This systematic review aims to map the existing evidence on direct neurotization techniques of the OOM, including nerve grafting, nerve transfers, and contralateral muscle transposition, and to describe their published effectiveness. Methods: A systematic search of PubMed, Embase, and supplemental searching with citation chaining using google scholar to identify studies detailing periocular neurotization techniques. Studies were included if they reported clear outcomes such as restored blinking or eye closure, improved corneal protection, and enhanced eyelid symmetry. Results: Of 857 screened studies, 6 met inclusion criteria, comprising 4 cohort studies, 1 case series, and 1 case report, with 106 patients eligible for detailed extraction. Direct neurotization of the OOM was associated with improvements in eye closure, blink reflex, and electromyographic data. Motor donor nerve selection was noted to affect outcomes significantly, with contralateral facial nerve branches yielding the highest blink improvement. Conclusions: This review highlights the emerging role of direct neurotization techniques in reinnervating the orbicularis oculi muscle for appropriate patients with facial nerve palsy. Future high-quality studies are needed to establish clear indications, long-term efficacy, and comparative advantages of direct OOM neurotization in clinical practice.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".