Proposed diagnostic and treatment paradigm for high-grade neurological complications of immune checkpoint inhibitors
Bibliographic record
Abstract
Immune checkpoint inhibitors such as antibodies to cytotoxic lymphocyte-associated protein 4 (ipilimumab) and programmed cell-death 1 (pembrolizumab, nivolumab) molecules have been used in non-small cell lung cancer, metastatic melanoma, and renal-cell carcinoma, among others. With these agents, immune-related adverse events (irAEs) can occur, including those affecting the neurological axis. In this review, high-grade neurological irAEs associated with immune checkpoint inhibitors including cases of Guillain-Barré syndrome (GBS) and myasthenia gravis (MG) are analyzed. Based on current literature and experience at our institution with 4 cases of high-grade neurological irAEs associated with immune checkpoint inhibitors (2 cases of GBS, 1 case of meningo-radiculitis, and 1 case of myelitis), we propose an algorithm for the investigation and treatment of high-grade neurological irAEs. Our algorithm incorporates both peripheral nervous system (meningo-radiculitis, GBS, MG) and central nervous system presentations (myelitis, encephalopathy). It is anticipated that our algorithm will be useful both to oncologists and neurologists who are likely to encounter neurological irAEs more frequently in the future as immune checkpoint inhibitors become more widely used.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".