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Record W3172287188 · doi:10.1093/ecco-jcc/jjab076.301

P174 Defining Crohn’s Disease Strictures Using Intestinal Ultrasound Compared to Histopathology

2021· article· en· W3172287188 on OpenAlexaff
Julian C. Cooper, Karen Danois, Konstantin Koro, Stephanie R. Wilson, Alexandra Medellin, Christopher Ma, Kerri L. Novak, Cynthia H. Seow, Gilaad G. Kaplan, Remo Panaccione, Cathy Lu

Bibliographic record

VenueJournal of Crohn s and Colitis · 2021
Typearticle
Languageen
FieldMedicine
TopicDiverticular Disease and Complications
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineCrohn's diseaseHistopathologyAnastomosisMuscle hypertrophyRadiologyFibrosisGold standard (test)Magnetic resonance imagingCrohn diseaseInflammatory bowel diseaseGastroenterologyInternal medicineDiseaseSurgeryPathology

Abstract

fetched live from OpenAlex

Abstract Background Fibrostenotic Crohn’s Disease (CD) is a challenging phenotype often requiring surgical resection. MRE (magnetic resonance enterography) is commonly used to diagnose CD strictures, but is limited by access, cost, and long acquisition time. The CONSTRICT consensus criteria for defining CD strictures is based only on MRE or CT (computed tomography) and includes: increased bowel wall thickness (BWT), narrowed luminal apposition, and pre-stenotic dilation > 3cm. This definition has not been studied using intestinal US (IUS). IUS is a cost-effective, easily repeatable, and well-tolerated tool with similar sensitivity and specificity to MRE in diagnosing and monitoring CD. In patients with a confirmed CD stricture on ileal resection (gold standard for diagnosis), we aim to assess the applicability of CONSTRICT criteria in diagnosing strictures with IUS. Methods In this pilot study, thirty CD patients who underwent small bowel resection from 2015–2019 with IUS (fasting, no oral contrast) within 6 months prior to surgery (excluding fistulizing disease) were randomly identified for chart review Stricture was confirmed on resected ileum as microscopic presence of fibrosis, muscle hypertrophy, inflammation, and macroscopic localized luminal narrowing with decreased circumference on pathology. Student’s t-tests, sensitivities and specificities were calculated for IUS in detecting strictures. Results 20 out of 30 patients had fibrostenosis on pathology and IUS. Only 40% (8/20) met CONSTRICT criteria for stricture diagnosis on IUS, despite having a stricture on pathology. All patients had elevated BWT and luminal narrowing, but 60% (12/20) of patients did not have prestenotic dilation > 3cm. Mean dilation was 2.9 cm (SD 1.38) and was significantly different from the mean stricture diameter of 1.3cm (SD 0.59 cm, p=0.0001, 95% CI: 0.9–2.2). Mean BWT was 8.7 mm (SD: 2.5, range 5–15), and mean luminal apposition was 2.3 mm (SD 1.2, range 0.2–5.8mm). IUS has a sensitivity of 95.2% (CI: 76.2 - 99.9%) and specificity of 66.7% (29.9 - 92.5%) in detecting strictures, when compared to the gold standard. Conclusion Diagnosis of fibrostenotic CD with CONSTRICT criteria using CT/MR may not be applicable to IUS. Only 40% of patients met criteria despite histologic-confirmed strictures. Unlike MR/CTE, IUS is not routinely performed with oral contrast, and as a result prestenotic dilation may be underestimated. Thus, perhaps additional criteria of stricture diameter < 50% of prestenotic dilation size is more appropriate for IUS. This pilot study provides the initial data to further delineate the definition of strictures on IUS for future validation and will inform both clinical practice and trial design.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.296
Teacher spread0.268 · 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 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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