Anti‐<i>Saccharomyces cerevisiae</i> Antibodies Status Is Associated with Oral Involvement and Disease Severity in Crohn Disease
Bibliographic record
Abstract
OBJECTIVES: To determine anti-Saccharomyces cerevisiae antibodies (ASCA) status and its relation to disease phenotype in patients with inflammatory bowel disease (IBD). PATIENTS AND METHODS: A total of 301 Scottish patients with early-onset IBD-197 Crohn disease (CD), 76 ulcerative colitis (UC), 28 indeterminate colitis (IC)-and 78 healthy control individuals were studied. ASCA status (IgA, IgG) was determined by enzyme-linked immunosorbent assay. ASCA status was then analyzed in relation to CD phenotype. RESULTS: Patients with CD had a higher prevalence of ASCA than patients with UC and healthy controls: 82/197 versus 12/76, odds ratio (OR) 3.80 (1.93-7.50) and 82/197 versus 6/78, OR 8.56 (3.55-20.62), respectively. Univariate analysis showed that positive ASCA status was associated with oral CD (17/25 vs 59/153, OR 3.39 [1.38-8.34]), perianal CD (39/77 vs 38/108, OR 1.89 [1.04-3.44]) and the presence of granulomata (63/132 vs 15/52, OR 2.25 [1.13-4.48]) and also with markers of disease severity: raised C-reactive protein (44/90 vs 12/49, OR 2.95[1.36-6.37]), hypoalbuminemia (44/85 vs 20/74, OR 2.28[1.19-4.37]), and surgery (27/49 vs 54/147, OR 2.11 [1.10-4.06]). From multivariate analysis, the presence of oral disease (adjusted P = 0.001, OR 22.22 [3.41-142.86]) and hypoalbuminemia (adjusted P = 0.01, OR 4.78 [1.40-16.39]) was found to be independently associated with ASCA status. No association was demonstrated between ASCA and IBD candidate genes. CONCLUSIONS: Patients with CD had a higher prevalence of ASCA than did other patients with IBD. ASCA status described patients with CD who had a specific phenotype, showing an association with markers of disease severity and oral CD involvement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".