Association Between Serological Markers and Crohn’s Disease Activity
Bibliographic record
Abstract
BACKGROUND: The aim was to study the association between six serological markers and Crohn's disease (CD) activity at an inflammatory bowel disease (IBD) referral center. METHODS: We designed a retrospective cohort study using adults (> 18 years) with CD followed for at least 1 year at University of Alabama at Birmingham. Baseline serological markers ASCA-IgA, ASCA-IgG, anti-OmpC IgA, anti-CBir1 IgG, anti-A4Fla2 IgG and anti-FlaX IgG were drawn at initial visit. Poisson regression was used to assess the longitudinal relationship between these markers drawn at baseline and rate of active clinical disease during follow-up. RESULTS: Each marker, from 135 patients, was categorized into high vs. low. A Poisson regression model adjusted for age, gender, race, duration of disease, obesity, proton pump inhibitor; steroid and thiopurine use, and disease location demonstrated that CD patients with high anti-CBir1 IgG at baseline were approximately twice more likely to have active clinical disease (incidence rate ratio (IRR) 2.06, 95% confidence interval (CI) 1.28 - 3.33, P = 0.0032). The unadjusted Poisson regression model for A4Fla2 IgG antibody level did suggest that a high A4Fla2 IgG at baseline was associated with a higher likelihood of active CD (IRR 1.64, 95% CI 1.07, 2.53, P = 0.0238) which however, upon adjustment based on effect size, was not significant. The other four antibodies did not appear to predict clinical course. CONCLUSIONS: High levels of anti-CBir1 IgG appear to be associated with a greater likelihood of active CD. Whether routine baseline testing for anti-CBir1 IgG to predict a more active clinical course is warranted needs more research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".