Magnetic Resonance Imaging Findings in Sudden Sensorineural Hearing Loss
Bibliographic record
Abstract
OBJECTIVE: To investigate the role of magnetic resonance imaging (MRI) in the diagnosis of sudden sensorineural hearing loss (SSNHL). METHODS: Fifty-four consecutive patients affected by SSNHL were investigated using brain MRI. MRI was performed with an eight-channel phased-array head coil to study the entire audiovestibular pathway and the whole brain. The protocol study consisted of a high-resolution study of the temporal bone, internal auditory canal (IAC), cerebellopontine angle (CPA), and brainstem combining 2 mm thin-slice axial T(2)-weighted two-dimensional fast spin echo (FSE) and fluid-attenuated inversion recovery (FLAIR) sequences, pre- and postcontrast (gadolinium-diethylenetriamine pentaacetic acid) administration fat-suppressed axial T(1)-weighted two-dimensional FSE sequences, and a T(2)*-weighted three-dimensional Fourier transformation-constructive interference in steady state sequence (FT-CISS) , with 0.4 mm ultrathin partitions. The rest of the brain was studied with a 4 mm axial T(2)-weighted FLAIR sequence. RESULTS: Thirty-one of 54 (57%) cases of SSNHL presented with MRI abnormalities. In 6 of 54 cases, the detected abnormality was directly correlated to the clinical picture (2 labyrinthine hemorrhage, 1 cochlear inflammation, 1 acoustic neuroma, 1 arachnoid cyst of the CPA, and 1 case of white matter lesions in the pons, compatible with demyelinating plaques along the central audiovestibular nervous pathway, as the first expression of multiple sclerosis). CONCLUSIONS: An extensive MRI study of the audiovestibular nervous pathway and of the whole brain, pre- and postparamagnetic contrast administration, is recommended to rule out the wide spectrum of abnormalities that can cause SSNHL.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".