Facial Reanimation After Intratemporal Facial Nerve Schwannoma Resection: A Systematic Review
Bibliographic record
Abstract
Objective: To systematically analyze the outcomes of reanimation techniques that have been described for patients undergoing non-fascicle sparing resection of intratemporal facial schwannomas. Methods: A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines of the PubMed, MEDLINE, and Cochrane Central Register of Controlled Trials databases. Results: Eight hundred forty studies were screened with 22 meeting inclusion criteria comprising 266 patients. Most facial nerve reanimations (81.2%) were performed using an interposition nerve graft. The remaining patients underwent hypoglossal-facial nerve transposition (13.9%), primary anastomosis (3.4%), and free muscle transfer (0.1%). Of the reported interposition grafts, the two most utilized were the great auricular (113/199) and sural (86/199) nerves. Interposition nerve grafts resulted in significantly better outcomes in facial nerve function postoperatively than hypoglossal-facial transposition (3.48 vs. 3.92; p < 0.01). There was no difference between interposition grafts. Conclusion: This study systematically reports that interposition nerve grafts, after resection of intratemporal facial schwannoma, result in superior outcomes than hypoglossal-facial nerve transposition in these patients.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".