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Record W4405463465 · doi:10.1080/17474124.2024.2444555

Advancements in the management of pediatric inflammatory bowel disease

2024· review· en· W4405463465 on OpenAlexafffund
Jonathan O’Donnell, Eric I. Benchimol

Bibliographic record

VenueExpert Review of Gastroenterology & Hepatology · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationPublic Health OntarioUniversity of Toronto
FundersHospital for Sick ChildrenUniversity of Toronto
KeywordsMedicineInflammatory bowel diseaseIntensive care medicineDiseaseDisease managementPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: The management of pediatric inflammatory bowel disease (PIBD) has drastically changed in the last decade. The limited availability of new biologics or small molecule therapies, and concerns about durability in children has necessitated the development of other advances in management to optimize care. AREAS COVERED: This review covers guidance for management targets and advances in optimizing biologic therapies, new medical therapies, adjuvant therapies, precision medicine and mental health concerns in PIBD. This review focused on recent advances and was not intended as a complete overview of the investigations and management of pediatric IBD. EXPERT OPINION: Advancements include standardization of treatment goals via a treat-to-target strategy, optimizing anti-TNF biologics through combination therapy or proactive drug monitoring, earlier initiation of treatment for Crohn's disease, the emergence of new biologic/advanced therapies and a growing focus on adjuvant therapies targeting the microbiome. Future progress relies on the inclusion of children/adolescents in clinical trials to facilitate faster regulatory approval for pediatric therapies and the integration of precision medicine and mental health screening to improve patient care and outcomes.

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 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)0.001
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 score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.014
GPT teacher head0.321
Teacher spread0.307 · 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 teacher head, not a consensus.

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

Citations4
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueExpert Review of Gastroenterology & HepatologySame topicInflammatory Bowel DiseaseFrench-language works237,207