Risk of immune-related colitis with PD-1/PD-L1 inhibitors vs chemotherapy in solid tumors: systems assessment
Bibliographic record
Abstract
Background: We performed a meta-analysis to evaluate the risk of immune-related colitis associated with PD1/PD-L1 inhibitors as compared to chemotherapy in solid tumor patients. Methods: Eligible studies were identified through a comprehensive search of multiple databases and included solid tumor patients in randomized controlled trials (RCTs) with PD-1/PD-L1 inhibitors. The data was analyzed by Stata version 12.0 software. Results: After exclusion of ineligible studies, 11 clinical trials were considered eligible for the meta-analysis, including 5751 patients. Compared with chemotherapy, the risk ratios (RRs) of all-grade colitis were significant for the PD-1 inhibitor subgroup (RR 2.69, 95% confidence interval (CI): 1.15-6.29, p=0.023), and for pembrolizumab subgroup (RR 3.17, 95% CI: 1.08-9.37, p=0.037), but not for nivolumab treatment and PD-L1 inhibitor (atezolizumab) treatment (RR 2.05, 95% CI: 0.52-8.13, p=0.305; RR 4.75,95% CI: 0.56-40.50, p=0.154, respectively). The RR of all-grade colitis was significant for PD-1/PD-L1 inhibitor in NSCLC (RR 4.34, 95% CI: 1.37-13.82, p=0.013), and not significant in melanoma (RR 2.11, p=0.285). Moreover, the RRs of all-grade diarrhea were significant for the PD-1 inhibitor subgroup (RR 0.61, 95% CI: 0.44-0.83, p=0.002), for the nivolumab subgroup (RR 0.54, 95% CI: 0.34-0.87, p=0.012), and for atezolizumab subgroup (RR 0.48, 95% CI: 0.25-0.89, p=0.021). The RR of high-grade diarrhea was significant for atezolizumab subgroup (RR 0.34, 95% CI: 0.12-0.94, p=0.037). Conclusions: Our meta-analysis demonstrates that compared with chemotherapy, pembrolizumab may result in a higher risk of all-grade immune-mediated colitis. PD-1/PD-L1 inhibitor treatment in NSCLC patients, but not in melanoma patients, increases the risk of all-grade colitis incidence.
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 | 0.047 | 0.063 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.080 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".