Delayed Facial Palsy After Resection of Vestibular Schwannoma: An Analysis of Long-term Facial Nerve Outcomes
Bibliographic record
Abstract
OBJECTIVES: 1) Identify clinical factors associated with delayed facial palsy (DFP) after microsurgical resection of vestibular schwannoma. 2) Determine whether DFP predicts worse facial nerve (FN) outcomes. METHODS: Adult patients (≥18 yrs) who underwent vestibular schwannoma resection between February 2008 and December 2017 were retrospectively reviewed. Postoperative House-Brackmann (HB) FN function was assessed on the day of surgery, daily during patients' inpatient admissions, and at postoperative clinic visits. Follow-up exceeded ≥12 months for all patients. DFP was defined as a decline (≥1 HB grade) in FN function (relative to the preoperative state) occurring between postoperative days 1 and 30. RESULTS: Two hundred ninety-one patients were analyzed. Mean age was 51.5 years (±12.3) and mean tumor size 20.6 mm (±10.8). Immediate FP occurred in 61 (21%) patients, and DFP occurred in 112 (38%) patients. Tumor size was largest in patients with immediate FP (p < 0.0001). On univariate analysis, DFP was associated with better final FN outcomes (OR 0.447, p = 0.0101) compared with immediate FP. Multivariate analysis, however, showed that timing of FP was no longer significant, whereas larger tumor size and preoperative HB2 function predicted worse FN outcomes (OR 2.718, p < 0.0001 and OR 9.196, p = 0.0039, respectively). In patients with DFP, longer time to onset of palsy predicted more favorable FN outcomes. CONCLUSIONS: When accounting for tumor size, the timing of onset of postoperative facial palsy does not predict final FN outcomes. In patients who develop DFP, the longer the interval between surgery and onset of weakness, the better the chances of good long-term FN function.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".