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Record W4407866440 · doi:10.1136/flgastro-2024-102971

Small bowel imaging in Crohn’s disease with a special focus on obesity, pregnancy and postsurgical assessment

2025· article· en· W4407866440 on OpenAlexaff
Patricia Kaazan, Aline Charabaty, Jane M. Andrews, Ramon Pathi, Leonie K. Heilbronn, Jonathan Segal, Gianluca Pellino, Kerri L. Novak, Christopher K. Rayner, Christen Barras

Bibliographic record

VenueFrontline Gastroenterology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePregnancyCrohn's diseaseFocus (optics)ObesityDiseaseInflammatory bowel diseaseIntensive care medicineRadiologyObstetricsInternal medicine

Abstract

fetched live from OpenAlex

Crohn’s disease (CD) is an immune-mediated, multisystem inflammatory disorder characterised by discontinuous transmural, sometimes granulomatous, inflammation of the gastrointestinal tract. Although it can occur anywhere in the gastrointestinal tract, it has a 70% predilection for the terminal ileum. Ileocolonoscopy with biopsy remains the gold standard for initial diagnosis and assessment of CD activity but has several limitations, including invasiveness, risk of complications and cost. With a shifting focus towards treatment targets including transmural healing, non-invasive imaging modalities are being used increasingly to assess the small bowel, particularly the terminal ileum. CT enterography, magnetic resonance enterography and gastrointestinal ultrasound are widely used for small bowel imaging in clinical practice and have relatively good sensitivity and specificity. Obesity is a growing problem for patients with CD and is associated with limitations in medical imaging. Equally, cross-sectional imaging in pregnant and postsurgical patients with CD has its own challenges. In this article, we review small bowel imaging in CD with a special focus on obesity, pregnancy and postsurgical assessment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.005
GPT teacher head0.228
Teacher spread0.224 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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