Anti‐<i>Saccharomyces cerevisiae</i> Antibodies as a Prognostic Biomarker in Children With Crohn Disease
Bibliographic record
Abstract
OBJECTIVE: Although anti-Saccharomyces cerevisiae antibodies (ASCAs) could be a useful biomarker in differentiating Crohn disease (CD) from ulcerative colitis (UC), their role as prognostic markers in children with CD has been underinvestigated. This longitudinal prospective observational study aimed to assess the prognostic value of ASCA status among children with CD managed using biologics. METHODS: The study population comprised children with inflammatory bowel disease diagnosed with CD from 2012 to 2018. Cox regression model with adjustment for a priori covariates was used to examine the response to anti-tumor necrosis factor (TNF) biological therapy among ASCA-positive patients in comparison to ASCA-negative patients. RESULTS: There were 273 measurements available from the study cohort comprising children with CD, who were followed up for a median duration of 14 months (interquartile range 5-42). ASCA-positive patients had a higher risk for moderate to severe clinical disease (odds ratio 2.88; 95% confidence interval [CI] 1.2-7.55) and extensive endoscopic distribution (odds ratio 3.30; CI 1.12-9.74) at baseline in comparison to ASCA-negative patients, respectively. In comparison to ASCA immunoglobulin G (IgG)-negative patients, ASCA IgG-positive patients who were treated with biologics had a significantly lower relapse rate (adjusted hazard ratio 0.12; CI 0.02-0.93). Ten (14%) patients had an unstable ASCA value with either ASCA immunoglobulin A or ASCA IgG status changing from positive to negative or vice versa. CONCLUSIONS: ASCA-positive children with CD present with more extensive (endoscopic) and clinically severe disease. ASCA IgG is a useful prognostic marker among children with CD who receive biologics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".