Treatment of facial nerve synkinesis with botulinum toxin. A systematic review
Bibliographic record
Abstract
Introduction:Facial synkinesis is a common chronic complication of facial nerve palsy, caused by aberrant nerve regeneration and leading to involuntary co-contractions, hypertonicity, and psychosocial burden. Botulinum toxin type A (botox) is widely used to reduce synkinetic activity, yet protocols vary considerably. This systematic review evaluates the efficacy, safety, and clinical practices of botox for facial synkinesis.Methods: A search of PubMed, Embase, ScienceDirect, and Web of Science identified original English-language studies involving adult patients with facial synkinesis treated with botox. Exclusion criteria included reviews, case reports, abstracts, guidelines, protocols, and pediatric studies. Two reviewers independently performed study selection, data extraction, and risk of bias assessment using the Newcastle–Ottawa Scale, with consensus from a third reviewer.Results: Forty-four studies (1994–2025; 1980 patients) were included. Synkinesis most commonly followed Bell’s palsy, Ramsay Hunt syndrome, or postoperative facial nerve injury. Botox dosing, injection sites, and guidance techniques varied widely, reflecting individualized treatment. Across studies, BoNT-A consistently improved synkinesis severity, facial symmetry, and quality-of-life metrics as assessed using scales such as Sunnybrook, House-Brackmann, SAQ, and FaCE. Benefits appeared within weeks and were maintained with repeated treatments. Adverse effects were mild and transient, including ptosis, dry eye, bruising, and temporary weakness, with no systemic complications reported.Conclusions: Botulinum toxin injection is an effective and safe therapy for facial synkinesis, improving both functional outcomes and patient-reported quality of life. However, heterogeneous protocols and outcome measures limit comparability. Future studies should implement standardized dosing strategies and validated assessment tools to support evidence-based treatment guidelines.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".