208 Immune checkpoint inhibitor-related neurotoxicity: a case series
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICI), monoclonal antibodies enhancing T cell responses against tumour cells, have revolutionised the treatment of cancers such as advanced melanoma, leading to enhanced survival. Their action, however, is not tumour-specific, and patients can develop multisystem immune related adverse events (irAE). Neurological irAEs have been reported in 1–14% of patients, depending upon the ICI used, and can affect any part of the neuro-axis. A recent case series from the Royal Marsden Hospital (RMH) identified 10 patients with neurotoxicity following ICI for advanced melanoma between 2010–15, specifically neuropathy (6), plexopathy (1) and aseptic meningitis (3). Exactly how neurological injury occurs, whether cell-, cytokine- or antibody-mediated, is unknown. We present early data from a newly established collaboration with RMH, aiming to clinically characterize these patients, and identify the cause of neurological injury. To date, we have advised on patients (age range 53–80) with myositis, Guillain-Barré (GBS)-like neuropathy, plexopathy, aseptic meningitis, and encephalitis following ICI (ipilimumab and/or nivolumab) for advanced melanoma. Features common to these patients include their subacute onset, time from ICI administration, and steroid responsiveness (including in GBS-like cases). The incidence of neurological irAEs following ICI will rise with increasing use, and is therefore of concern to practicing neurologists.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".