011 A case of a neurological immune-related adverse event associated with ipilimumab/nivolumab
Bibliographic record
Abstract
Abstract We present an unusual movement disorder due to immune checkpoint inhibitors which responded rapidly to prednisolone. Background Immune checkpoint inhibitors (ICIs) have revolutionised the scope of cancer therapy but can induce autoimmune effects on healthy organs, termed immune-related adverse events (irAEs). Their pathophysiology relies on the same mechanisms that confer anti-tumour activity and can affect any organ in the body. Neurological irAEs (n-irAEs) although rare, are potentially fatal. Differentiating n-irAEs from paraneoplastic syndromes and other neurological disorders can be challenging. Case We present a case of n-irAE in a patient with metastatic renal cell carcinoma treated with two cycles of ipilimumab/nivolumab. Following the second cycle, the patient developed subacute onset of postural and rest tremor, predominantly affecting her upper limbs, head and tongue, of variable amplitude, with bradykinesia of foot tapping. Her gait was broad-based. MRI brain was unremarkable. CSF was lymphocytic (WBC 103 cells, 90% lymphocytes) with elevated protein (1.30g/L), normal CSF:serum glucose ratio, negative CSF culture and viral PCR. She was treated for ICI-associated neurotoxicity and responded rapidly to high-dose steroids. Conclusion Physicians should be aware of the diverse presentations of n-irAEs, including unusual movement disorders. Close collaboration between neurologists and oncologists is imperative in the management of these patients.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| 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".