Risk of Neurological Toxicities Following the Use of Different Immune Checkpoint Inhibitor Regimens in Solid Tumors
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to assess risk of neurological toxicities following the use of different immune checkpoint inhibitor (ICI) regimens in solid tumors. METHODS: Pubmed, Embase, and ClinicalTrials.gov databases were searched for publications, and data were analyzed using Review Manager 5.3 software to compare the risk of immune-related and nonspecific neurological complications potentially triggered by ICIs to controls. RESULTS: In total 23 randomized clinical trials comprising 11,687 patients were included in this meta-analysis. Patients with PD-L1 (OR, 0.29; 95% confidence interval [CI], 0.18-0.48; P<0.01) or programmed cell-death protein 1 (PD-1) inhibitor (OR, 0.21; 95% CI, 0.14-0.31; P<0.01) were less likely to develop any-grade peripheral neuropathy than chemotherapy, while the risk of grade 3-5 was also smaller for PD-1 inhibitor (OR, 0.16; 95% CI, 0.05-0.54; P=0.003). Combination therapy with CTLA4 and PD-1 inhibitor did not significantly increase the risk of any-grade (OR, 0.83; 95% CI, 0.21-3.32; P>0.05) or grade 3-5 (OR, 1.4; 95% CI, 0.2-9.61; P>0.05) peripheral neuropathy compared to monotherapy with CTLA4 or PD-1 inhibitor. However, difference in risk of immune-related adverse events (irAEs) involving central nervous system did not reach statistical significance in patients with different ICI regimens compared those under chemotherapy. Additionally, risk of experiencing paresthesia was in line with that of peripheral neuropathy (OR, 0.42; 95% CI, 0.28-0.62; P<0.01). CONCLUSIONS: This meta-analysis shows that PD-L1/PD-1 and CTLA4 inhibitor have decreased risk of peripheral neuropathy compared to chemotherapy, while combination therapy with CTLA4 and PD-1 inhibitor have no difference in neurological toxicities compared to monotherapy with CTLA4 or PD-1 inhibitor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.021 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".