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Record W2974496516 · doi:10.1177/1756284819870911

Impact of the gut microbiota on immune checkpoint inhibitor-associated toxicities

2019· review· en· W2974496516 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTherapeutic Advances in Gastroenterology · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health Research
KeywordsMedicineInflammatory bowel diseaseGut floraImmune systemDiarrheaColitisImmunologyDiseaseDiscontinuationGastroenterologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.342
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it