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Record W2922407286 · doi:10.1093/jcag/gwz006.070

A71 ANTI-SACCHAROMYCES CEREVISIAE ANTIBODIES AS A PROGNOSTIC BIOMARKER IN CHILDREN WITH CROHN’S DISEASE

2019· article· en· W2922407286 on OpenAlexaffabout
Abin Chandrakumar, M. Georgy, Prasoon Agarwal, Geert ‘t Jong

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2019
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsChildren's Hospital Research Institute of ManitobaUniversity of Manitoba
Fundersnot available
KeywordsMedicineUlcerative colitisInternal medicineBiomarkerInflammatory bowel diseaseDiseaseCrohn's diseaseGastroenterologyPopulationImmunologyBiology

Abstract

fetched live from OpenAlex

Inflammatory bowel disease (IBD) is a collective term for diseases involving inflammation of the gastrointestinal tract, among which Crohn’s disease (CD) and ulcerative colitis (UC) are the two main sub-categories. Although Anti-Saccharomyces cerevisiae antibodies (ASCA) could be a useful biomarker in differentiating CD from ulcerative colitis UC, their role as prognostic markers in children with CD has been under-investigated. Our longitudinal study aimed to assess the stability of ASCA status over time and the possibility of an association between ASCA-positive childhood CD and disease relapse during clinical follow-up. We also investigated the role of ASCA status as a prognostic marker of response to Anti-TNF biological therapy among children with CD. The study population comprised of children with inflammatory bowel disease (IBD) diagnosed with CD from 2012 to 2018. Cox-regression model with adjustment for a priori covariates was used to examine the risk of disease relapse and response to biological therapy among ASCA positive patients in comparison to ASCA negative patients. The models were adjusted for age at diagnosis, sex, history of CD in the first-degree relatives, luminal disease distribution at baseline, pharmacological therapy and baseline clinical disease activity. A total of 273 measurements from 72 CD patients (mean age at diagnosis of 12.17±3.17 years), who were followed up for a median duration of 14 months (IQR: 5–42) were included. ASCA positive patients had a higher risk for moderate to severe clinical disease and extensive distribution at baseline in comparison to ASCA negative patients, (odds ratio (OR): 2.88; 95% confidence interval (CI): 1.2–7.55) (OR: 3.30; 95% CI: 1.12–9.74) respectively. In comparison to ASCA IgG negative patients, ASCA IgG positive patients who were treated with biologics had a significantly lower relapse rate (aHR: 0.12; 95% CI: 0.02–0.93). Ten (14%) patients had an unstable ASCA value with either ASCA IgA or ASCA IgG status changing from positive to negative or vice versa. ASCA positive children with CD present with more extensive and clinically severe disease. ASCA IgG is a useful prognostic marker among children with CD who receive biologics. Survival plots for disease relapse during follow-up among biological therapy naïve children with Crohn’s disease (a and c) compared to those who were treated with biological therapy (b and d). (adjusted for age at diagnosis, sex, family history of IBD, luminal disease distribution at baseline, non-biologic pharmacological therapy and baseline disease severity) Children’s Hospital Research Institute of Manitoba

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)0.003
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.241
Teacher spread0.234 · 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".

Quick stats

Citations0
Published2019
Admission routes2
Has abstractyes

Explore more

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