High resolution magnetic resonance imaging for exposing facial nerve zonal vulnerability to microbleeds: A rare cause of facial palsy
Bibliographic record
Abstract
Background We report a case of hypertensive microbleeds strategically located at the attached segment (AS) and root entry zone (REZ) at the left facial nerve causing facial paralysis. Case Report A 60-year-old woman presented with sudden onset left facial paralysis. Medical history was significant for poorly controlled hypertension secondary to bilateral adrenal hyperplasia (primary hyperaldosteronism). The patient was initially treated for presumptive Bell's palsy. Subsequent magnetic resonance imaging of the brain and internal auditory canal showed two microbleeds at the left cerebellopontine angle. Dedicated coronal T1 magnetization prepared rapid acquisition gradient echo and T2 sampling perfection with application optimized contrasts using different flip angle evolution sequences revealed two acute microbleeds located at the attached AS and REZ of the left facial nerve. The patient experienced only partial recovery from House-Brackmann grade IV facial paralysis at presentation to a House-Brackmann grade III facial paralysis at 1 year of follow up. Conclusions To the best of the authors' knowledge, this is the first reported case of facial paralysis caused by microbleeds directly affecting the vulnerable AS and REZ facial nerve segments. We discuss the zonal microanatomy of the facial nerve and the crucial role of high resolution MRI for diagnosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".