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Record W2896179149 · doi:10.1097/aci.0000000000000486

Very early onset IBD: novel genetic aetiologies

2018· review· en· W2896179149 on OpenAlexafffund
Vritika Batura, Aleixo M. Muise

Bibliographic record

VenueCurrent Opinion in Allergy and Clinical Immunology · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchLeona M. and Harry B. Helmsley Charitable Trust
KeywordsMedicinePenetranceInflammatory bowel diseaseDiseaseMechanism (biology)Genome-wide association studyCrohn's diseaseEtiologyBioinformaticsImmunologyIntensive care medicineGeneticsPhenotypeGenePathologyGenotypeBiologySingle-nucleotide polymorphism

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To summarize the current understanding and recent advances on the genetic aetiology in the pathogenesis of very early onset inflammatory bowel disease (VEO-IBD). RECENT FINDINGS: IBD is a chronic disorder of the gastrointestinal tract whose manifestation is a result of complex interactions between genetics, environment, immune system and microbial flora. Over 230 IBD risk loci have been reported in genome wide association studies but the genetic contribution of the majority of these loci in the manifestation of IBD is very low. Patients with VEO-IBD present with a more severe disease than older patients, characterized by poor prognosis and failure of conventional therapy. Recent studies have reported several monogenic diseases with high penetrance that present with IBD and IBD-like intestinal manifestations and overlap with primary immunodeficiencies. Increasing body of evidence supports a prominent role of genetics in the onset of VEO-IBD. New genetic variants and diagnoses in VEO-IBD are reviewed and current challenges in therapy with potential strategy to manage the disease are discussed. SUMMARY: Functional analysis of the genes implicated in monogenic IBD has increased the understanding of the underlying pathobiological mechanism of the disease. This knowledge can be used to personalize medicine for specific patients, improving the standard of care and quality of life.

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)0.000
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
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 head0.093
GPT teacher head0.403
Teacher spread0.309 · 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

Citations21
Published2018
Admission routes2
Has abstractyes

Explore more

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