Correlation of Facial Nerve Function and Electrical Stimulation During Vestibular Schwannoma Surgery
Bibliographic record
Abstract
OBJECTIVE: The aim of the present study is to identify electrophysiological parameters in facial nerve monitoring that provide the best predictive values in vestibular schwannoma surgery. STUDY DESIGN: Retrospective study. SETTING: Tertiary care hospital. METHODS: In total, 76 patients undergoing translabyrinthine vestibular schwannoma resection were included. Facial nerve monitoring was conducted with free-running electromyography and direct electrical stimulation. Direct electrical stimulation protocol was predicated on a constant-current delivery at the root exit zone before and after tumor excision; specifically, recordings were made with increasing current settings: 0.05, 0.1, and 0.3 mA. The maximum amplitude measured from either of the two muscle groups was used for further correlations. Facial nerve function was graded according to the House-Brackmann (HB) scale before and after tumor excision. RESULTS: Immediate postoperative facial nerve outcomes were HB I (21%), HB II (40%), HB III (18%), HB IV (9%), and HB V (12%). Long-term facial nerve outcomes were distributed as follows: 66% HB I, 21% HB II, 12% HB III, and 1% HB IV. Results show that patients with a higher amplitude ≥ 1024 μV at orbicularis oris and/or frontalis had a >90% estimated probability of an HB I-II score in the long term. In contrast, patients with a low amplitude of, for example, 128 μV at orbicularis oris had an estimated 53% probability of an HB score of III or IV in the long term. CONCLUSION: Facial nerve monitoring is an indispensable objective measure during vestibular schwannoma surgery. Higher amplitudes lead to more favorable results for patients. Amplitudes 500 and 1000 μV seem to be useful cutoff points to help guide patient expectations with respectively >84% and >90% estimated probability of an HB I-II score in the long term.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".