Higher titers of anti-Saccharomyces cerevisiae antibodies IgA and IgG are associated with more aggressive phenotypes in Romanian patients with Crohn's disease.
Bibliographic record
Abstract
BACKGROUND AND AIMS: Serological markers have been widely used for diagnostic purposes and disease stratification in inflammatory bowel diseases (IBD). The aim of this study was to investigate the seroprevalence and the correlations of anti-Saccharomyces cerevisiae antibodies (ASCA) titers with different clinical phenotypes in Romanian patients with Crohn's disease (CD). METHODS: The study included 107 CD and 86 ulcerative colitis (UC) patients from the Gastroenterology Departments of three University Hospitals, and 60 healthy subjects. ASCA IgA and IgG titers were determined using ELISA test. For CD patients the phenotype was established according to the Montreal classification. The differences in ASCA titers for different CD phenotypes were assessed using the Mann-Whitney U test. RESULTS: ASCA prevalence was 33.6% in CD group, 12.8% in UC group and 6.6% in the control group. Significantly higher IgA (p=0.05) and IgG (p=0.03) titers were found in patients from the Montreal A1+A2 groups (age at onset below 40) compared with the older patients (A3). Higher titers were found in patients with extensive ileo-colonic lesions (L3) and upper gastrointestinal tract involvement (L4) than in patients having only colonic disease (L2). Significantly higher IgA (p=0.03) and IgG (p=0.03) titers were observed in patients with stenosing (B2) and penetrating (B3) disease compared with the nonstricturing, nonpenetrating (B1) phenotype. No correlation between ASCA titers and disease duration was found. CONCLUSION: ASCA seropositivity in Romanian CD patients is lower than in Western Europe. Higher ASCA IgA and IgG titers are associated with a younger age at diagnosis and more aggressive phenotypes.
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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".