Immune-mediated enterocolitis is associated with immune checkpoint inhibitors: A pharmacovigilance study from the FDA Adverse Event Reporting System (FAERS) database
Bibliographic record
Abstract
PURPOSE: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment by demonstrating significant efficacy across multiple malignancies. However, by interfering with immune regulatory pathways, they can lead to immune-related adverse events (irAEs), including immune-mediated enterocolitis. This study aimed to evaluate the real-world risk of immune-mediated enterocolitis across different ICIs using data from the FDA's Adverse Event Reporting System (FAERS). METHODS: A disproportionality analysis was conducted using FAERS data to assess the association between different ICIs and the risk of immune-mediated enterocolitis. The risk was analyzed across three ICI classes: cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitors, programmed death-1 (PD-1) inhibitors, and programmed death-ligand 1 (PD-L1) inhibitors. RESULTS: The analysis revealed significant variability in the risk of immune-mediated enterocolitis among ICIs. CTLA-4 inhibitors, particularly tremelimumab and ipilimumab, exhibited the strongest association with enterocolitis. Among PD-1 inhibitors, nivolumab demonstrated the highest risk, while PD-L1 inhibitors, including durvalumab and atezolizumab, had a lower but still notable association. CONCLUSIONS: These findings underscore the need for vigilant monitoring and early intervention in patients receiving ICIs. The differential risk profile among ICIs suggests that physicians should consider enterocolitis risk when selecting and managing immunotherapy regimens.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".