Management and outcomes of facial nerve injury following rhytidectomy: a systematic review
Bibliographic record
Abstract
BACKGROUND AND AIM: Facial nerve injury is a critical complication of rhytidectomy, affecting patient outcomes and satisfaction. Despite its importance, standardized management strategies remain limited. This systematic review evaluates current evidence on the management, outcomes, and prevention of facial nerve injuries in rhytidectomy, with stratification by injury severity to enhance clinical applicability. METHODS: In this study, PubMed, Embase, and the Cochrane Library were searched from inception to July 2025, identifying 20 studies that met the inclusion criteria. The quality of the studies was assessed using AMSTAR 2 and the Newcastle-Ottawa Scale. Additionally, the review was conducted in accordance with the PRISMA guidelines to ensure transparency and accuracy in reporting the results. RESULTS: The incidence of facial nerve injury ranged from 0.5% to 5%, with 70% of patients achieving full recovery within six months through conservative treatments (corticosteroids, physiotherapy). Management and outcomes varied by injury severity: neuropraxia (80-90% of cases) typically resolved conservatively, while axonotmesis or neurotmesis required surgical interventions (e.g., nerve repair) or adjunct therapies (e.g., botulinum toxin). Preventive measures, such as meticulous surgical techniques and awareness of facial danger zones, were effective. Intraoperative nerve monitoring showed potential but needs further validation. CONCLUSIONS: Conservative management suffices for most cases, particularly neuropraxia, yet 10% of patients experience persistent deficits, underscoring the need for severity-stratified approaches. Prospective multicenter registries with standardized outcome measures, individual patient data meta-analyses, and Bayesian hierarchical modeling are essential to address evidence gaps and enhance 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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| 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) | 0.004 | 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".