Exploring a Rarity: Incidence of and Therapeutic Approaches for Neurological Complications and Hypophysitis in Cancer Patients on Immune Checkpoint Inhibitors—A Single-Center Study
Bibliographic record
Abstract
Immune checkpoint inhibitors, such as anti-PD-1 and anti-CTLA-4 inhibitors, have become the standard of care for many cancer types. However, they induce immune-related adverse events (irAEs), including neurotoxicity and hypophysitis. The incidence and outcomes of neurotoxicity and hypophysitis in patients treated with immune checkpoint inhibitors are not well established. We conducted a retrospective study of 812 patients with solid cancers who received immune checkpoint inhibitors at the University General Hospital of Ioannina between January 2018 and January 2023. We assessed demographic and clinical data, including the severity of symptoms, treatment regimen, other irAEs, resolution type and time, and death. Two patients experienced neurotoxicity and two hypophysitis. All four patients required inpatient administration and received corticosteroids or/and hormone replacement. Three patients responded to the initial therapy, experiencing full recovery, while one patient was corticosteroid-resistant, and immunoglobin G was administered. Two patients never received immunotherapy after their toxicity due to the severity of symptoms; one patient continued monotherapy with nivolumab, changing from combination therapy with ipilimumab-nivolumab, while the fourth patient continued his initial treatment with nivolumab. Our study suggests that the incidence of neurotoxicity and hypophysitis in patients treated with immune checkpoint inhibitors is low, but careful monitoring and prompt treatment with corticosteroids are necessary for effective management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".