Combined Serological, Genetic, and Inflammatory Markers Differentiate Non-IBD, Crohnʼs Disease, and Ulcerative Colitis Patients
Bibliographic record
Abstract
BACKGROUND: Previous studies have demonstrated that serological markers can assist in diagnosing inflammatory bowel disease (IBD). In this study, we aim to build a diagnostic tool incorporating serological markers, genetic variants, and markers of inflammation into a computational algorithm to examine patterns of combinations of markers to (1) identify patients with IBD and (2) differentiate patients with Crohn's disease (CD) from ulcerative colitis (UC). METHODS: In this cross-sectional study, patient blood samples from 572 CD, 328 UC, 437 non-IBD controls, and 183 healthy controls from academic and community centers were analyzed for 17 markers: 8 serological markers (ASCA-IgA, ASCA-IgG, ANCA, pANCA, OmpC, CBir1, A4-Fla2, and FlaX), 4 genetic markers (ATG16L1, NKX2-3, ECM1, and STAT3), and 5 inflammatory markers (CRP, SAA, ICAM-1, VCAM-1, and VEGF). A diagnostic Random Forest algorithm was constructed to classify IBD, CD, and UC. RESULTS: Receiver operating characteristic analysis compared the diagnostic accuracy of using a panel of serological markers only (ASCA-IgA, ASCA-IgG, ANCA, pANCA, OmpC, and CBir1) versus using a marker panel that in addition to the serological markers mentioned above also included gene variants, inflammatory markers, and 2 additional serological markers (A4-Fla2 and FlaX). The extended marker panel increased the IBD versus non-IBD discrimination area under the curve from 0.80 (95% confidence interval [CI], ±0.05) to 0.87 (95% CI, ±0.04; P < 0.001). The CD versus UC discrimination increased from 0.78 (95% CI, ±0.06) to 0.93 (95% CI, ±0.04; P < 0.001). CONCLUSIONS: Incorporating a combination of serological, genetic, and inflammation markers into a diagnostic algorithm improved the accuracy of identifying IBD and differentiating CD from UC versus using serological markers alone.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".