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Record W4389309449 · doi:10.1080/19490976.2023.2287073

Gut microbiome-associated predictors as biomarkers of response to advanced therapies in inflammatory bowel disease: a systematic review

2023· review· en· W4389309449 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.

Bibliographic record

VenueGut Microbes · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMicrobiomeInflammatory bowel diseaseButyrateDiseaseBiologyMetabolomicsGut floraGut microbiomeSystematic reviewBioinformaticsImmunologyMEDLINEMedicineInternal medicine

Abstract

fetched live from OpenAlex

Loss of response to therapy in inflammatory bowel disease (IBD) has led to a surge in research focusing on precision medicine. Three systematic reviews have been published investigating the associations between gut microbiota and disease activity or IBD therapy. We performed a systematic review to investigate the microbiome predictors of response to advanced therapy in IBD. Unlike previous studies, our review focused on predictors of response to therapy; so the included studies assessed microbiome predictors before the proposed time of response or remission. We also provide an update of the available data on mycobiomes and viromes. We highlight key themes in the literature that may serve as future biomarkers of treatment response: the abundance of fecal SCFA-producing bacteria and opportunistic bacteria, metabolic pathways related to butyrate synthesis, and non-butyrate metabolomic predictors, including bile acids (BAs), amino acids, and lipids, as well as mycobiome predictors of response.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.274
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.013
GPT teacher head0.314
Teacher spread0.300 · 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