Risk of Immune-Related Pancreatitis in Patients with Solid Tumors Treated with Immune Checkpoint Inhibitors: Systematic Assessment with Meta-Analysis
Bibliographic record
Abstract
We performed a systematic review and meta-analysis to determine the risk of immune-related pancreatitis associated with the treatment by immune checkpoint inhibitors (ICIs) for solid tumors. Eligible studies were selected from multiple databases including phase II/III randomized controlled trials (RCTs) with ICIs in solid tumor patients. The data were analyzed with Stata version 12.0 software. After excluding ineligible studies, a total of 15 clinical trials were considered eligible for the meta-analysis, which included 9099 patients. Compared with chemotherapy or placebo, the risk ratio (RR) for all-grade lipase elevation after CTLA-4 inhibitor treatment was 1.05 (95% confidence interval (CI): 1.01–2.24, p=0.047 ). However, the risk for pancreatitis after ICI treatment in any subgroup was not significantly higher than that after control therapy. In addition, compared with ipilimumab/nivolumab alone, the RR for all-grade and high-grade lipase elevation under combination treatment of nivolumab and ipilimumab was 6.43 (95% CI: 1.43–28.99, p=0.015 ) and 6.44 (95% CI: 1.39–29.79, p=0.017 ), respectively, and the RR for all-grade amylase elevation under combination treatment was 6.08 (95% CI: 1.51–24.44, p=0.011 ). Our meta-analysis has demonstrated that both CTLA-4 inhibitors alone and combination treatment of nivolumab and ipilimumab could increase the risk of amylase or lipase elevation, but not significantly increase the risk of pancreatitis when compared with controls.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.065 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".