Impact of the gut microbiota on immune checkpoint inhibitor-associated toxicities
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) have transformed the treatment of patients with advanced cancers. However, the majority of patients do not respond or develop early progressive disease. A substantial number also develop immune-mediated toxicities that may lead to early treatment discontinuation. Gastrointestinal toxicities in the form of diarrhea and colitis are common and may resemble that observed in patients with inflammatory bowel disease (IBD). Alterations in the gut microbiota are thought to play an important role in mediating the intestinal inflammation that is associated with immune-mediated colitis. In this review, the authors' objective is to provide an overview of the gastrointestinal and hepatic toxicities that can be seen with ICIs and discuss the interactions between gut microbiota and the immune response. The authors also highlight the potential role for fecal microbial transfer (FMT) as an approach to improve therapeutic efficacy and decrease toxicity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".