Predicting Long-Term Facial Nerve Outcomes After Resection of Vestibular Schwannoma
Bibliographic record
Abstract
OBJECTIVES: 1) Describe the effect of tumor size on facial nerve (FN) outcomes after microsurgical resection of vestibular schwannoma (VS).2) Describe the effect of surgical approach, preoperative radiation, and early postoperative facial function on long-term FN outcomes. STUDY DESIGN: Retrospective analysis. SETTING: Tertiary referral center. PATIENTS: Adult (≥18 yr) patients underwent translabyrinthine or retrosigmoid VS resection by a single neurotologist and single neurosurgeon between February 2008 and December 2017. MAIN OUTCOME MEASURES: Long-term FN outcomes (≥12 mo) according to House-Brackmann (HB) grade. RESULTS: During the study period, 350 patients underwent VS resection, of whom 290 met inclusion criteria. Translabyrinthine surgery was performed in 54% (n = 158) and retrosigmoid in 45% (n = 131). One patient underwent a combined approach. Among patients who underwent retrosigmoid approach, none had a tumor more than 30 mm. Gross total resection was achieved in 98% (n = 283). Long-term HB1-2 function was achieved in 90% (n = 261). On univariate analysis, tumor size (per cm increase), history of preoperative radiation, and worse HB score at discharge predicted worse FN function. Multivariate analysis showed that tumor size (per cm increase) and history of radiation were independent predictors of FN function. For patients with tumors less than 30 mm, multivariate analysis of tumor size and surgical approach was performed; tumor size remained predictive of worse FN function (odds ratio [OR] 2.362, p = 0.0035), whereas surgical approach was not significantly predictive (p = 0.7569). CONCLUSION: Tumor size and history of radiation predict long-term FN function after VS resection. When accounting for tumor size, the translabyrinthine and retrosigmoid approaches yield equivalent FN results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".