MétaCan
Menu
Back to cohort
Record W2564762943 · doi:10.1172/jci88879

Immunologic impact of the intestine in metabolic disease

2017· review· en· W2564762943 on OpenAlexafffund
Daniel A. Winer, Shawn Winer, Helen J. Dranse, Tony K.T. Lam

Bibliographic record

VenueJournal of Clinical Investigation · 2017
Typereview
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsSt. Michael's HospitalToronto General HospitalDiabetes CanadaUniversity of Toronto
FundersToronto General Hospital Research Institute, University Health NetworkCanadian Institutes of Health ResearchUniversity of TorontoJ.P. Bickell FoundationCanadian Diabetes Association
KeywordsImmune systemInflammationInsulin resistanceSystemic inflammationObesityDiabetes mellitusImmunityImmunologyCarbohydrate metabolismDiseaseAcquired immune systemBiologyEndocrinologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Obesity and diabetes are associated with increased chronic low-grade inflammation and elevated plasma glucose levels. Although inflammation in the fat and liver are established features of obesity-associated insulin resistance, the intestine is emerging as a new site for immunologic changes that affect whole-body metabolism. Specifically, microbial and dietary factors incurred by diet-induced obesity influence underlying innate and adaptive responses of the intestinal immune system. These responses affect the maintenance of the intestinal barrier, systemic inflammation, and glucose metabolism. In this Review we propose that an understanding of the changes to the intestinal immune system, and how these changes influence systemic immunity and glucose metabolism in a whole-body integrative and a neuronal-dependent network, will unveil novel intestinal pathologic and therapeutic targets for diabetes and obesity.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.427
GPT teacher head0.579
Teacher spread0.152 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations77
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Clinical InvestigationSame topicDietary Effects on HealthFrench-language works237,207