Very high-dose methylprednisolone for treatment of nivolumab-induced limbic encephalitis: A case report
Bibliographic record
Abstract
INTRODUCTION: Nivolumab is a programmed death 1 (PD-1) inhibitor approved by the Food and Drug Administration (FDA) for the treatment of eight different cancers including metastatic melanoma. Immune checkpoint blockade may lead to a range of neurologic immune-related adverse events (irAEs) with severity varying from mild to life-threatening, including encephalitis. CASE REPORT: We describe a case of a 68-year-old man who developed alteration in mental status, physical weakness and fatigue after nine cycles of nivolumab 3 mg/kg every two weeks. These symptoms were compatible with a clinical diagnosis of autoimmune limbic encephalitis, although no specific antibodies were detected and the initial MRI was normal. MANAGEMENT AND OUTCOME: The patient received intravenous methylprednisolone 1 g daily for 5 days, which was then converted to a maintenance dose of oral prednisone. The patient made a full clinical recovery but relapsed clinically upon steroid tapering, while hypersignal in the left mesial temporal suggestive of limbic encephalitis was observed on repeated MRI. DISCUSSION: Because of the prevailing usage of nivolumab in many cancer protocols, this case highlights the importance of rapidly recognising neurological impairment in patients treated with nivolumab and of initiating very high doses of corticosteroids.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".