Magnetic resonance imaging of facial nerve schwannoma
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: This study characterizes the magnetic resonance (MR) appearances of facial nerve schwannoma (FNS). We hypothesize that the extent of FNS demonstrated on MR will be greater compared to prior computed tomography studies, that geniculate involvement will be most common, and that cerebellar pontine angle (CPA) and internal auditory canal (IAC) involvement will more frequently result in sensorineural hearing loss (SNHL). STUDY DESIGN: Retrospective study. METHODS: Clinical, pathologic, and enhanced MR imaging records of 30 patients with FNS were analyzed. Morphologic characteristics and extent of segmental facial nerve involvement were documented. RESULTS: Median age at initial imaging was 51 years (range, 28-76 years). Pathologic confirmation was obtained in 14 patients (47%), and the diagnosis reached in the remainder by identification of a mass, thickening, and enhancement along the course of the facial nerve. All 30 lesions involved two or more contiguous segments of the facial nerve, with 28 (93%) involving three or more segments. The median segments involved per lesion was 4, mean of 3.83. Geniculate involvement was most common, in 29 patients (97%). CPA (P = .001) and IAC (P = .02) involvement was significantly related to SNHL. Seventeen patients (57%) presented with facial nerve dysfunction, manifesting in 12 patients as facial nerve weakness or paralysis, and/or in eight with involuntary movements of the facial musculature. CONCLUSIONS: This study highlights the morphologic heterogeneity and typical multisegment involvement of FNS. Enhanced MR is the imaging modality of choice for FNS. The neuroradiologist must accurately diagnose and characterize this lesion, and thus facilitate optimal preoperative planning and counseling.
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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 | 0.002 |
| 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) | 0.002 | 0.001 |
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".