Perilymphatic Signal Changes in Vestibular Schwannoma: A Potential Biomarker of Progressive Hearing Loss?
Bibliographic record
Abstract
OBJECTIVE: Vestibular schwannomas influence the magnetic resonance (MR) signal intensity (SI) in the vestibular cistern and cochlear perilymph. The aim of this study is to evaluate the relationship between perilymphatic signal changes on gradient-echo T2-weighted 3 T MR sequence and the clinical symptoms. STUDY DESIGN: Retrospective case-control study. SETTING: The study was conducted at the Institute of Image-Guided Surgery in Strasbourg, France. METHODS: Patients with vestibular schwannoma who underwent magnetic resonance imaging at our institution between 2008 and 2016 were retrospectively reviewed. A control group consisted of individuals without past or present otological symptoms. The vestibular schwannomas were divided into three groups, based on the degree of internal auditory canal obstruction. The SI ratios of the vestibular cistern to cerebrospinal fluid (CSF) and cochlea to CSF were compared with clinical data. RESULTS: We included 172 patients with vestibular schwannoma and 61 controls. Vestibular schwannoma was associated with a significant decrease of the SI ratio of the vestibular cistern to CSF (0.716 ± 0.297 vs 1.06 ± 0.21, P = .004) and cochlea to CSF (0.66 ± 0.199 vs 0.903 ± 0.011, P = .004) compared to controls, with significant negative correlation between both the SI ratios of the vestibular cistern and cochlea to CSF with tumor volume (P < .001). Among all the symptoms studied, the SI ratio of the cistern normalized by CSF was significantly associated with progressive hearing loss (P = .003). CONCLUSION: Perilymphatic vestibular cistern and cochlear SI changes appear to be a promising noninvasive biomarker for hearing impairment in vestibular schwannoma.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".