Serological markers associated with disease behavior and response to anti‐tumor necrosis factor therapy in ulcerative colitis
Bibliographic record
Abstract
BACKGROUND AND AIM: Information is limited on the relationship between serological markers and disease behavior and anti-tumor necrosis factor-α (anti-TNF) therapy response in ulcerative colitis (UC). This study aimed to determine the association between serological markers and unfavorable UC behavior defined as need for colectomy or UC-related hospitalization. The association between serological markers and requirement for and outcome of anti-TNF therapy was also evaluated. METHODS: Two hundred thirty patients were studied. Requirement for colectomy, UC-related hospitalization, and anti-TNF therapy were documented. Response to anti-TNF therapy at 1 year and rates of therapy discontinuation were recorded. Titers of perinuclear anti-neutrophil cytoplasmic antibodies (pANCAs), anti-Saccharomyces cerevisiae antibody (ASCA), and antibody to Escherichia Coli outer membrane porin (anti-OmpC) were determined. Antibody reference ranges were used to dichotomize subjects into seropositive and seronegative groups. Where multiple tests were performed, P-values were Bonferroni corrected (pcorr). RESULTS: Extensive colitis was associated with requirement for colectomy and UC-related hospitalization, HR 7.7 (95% confidence interval [CI] 1.9-32.2) pcorr = 0.03 and HR 2.7 (95% CI 1.5-4.6), pcorr = 0.006, respectively. No serological variable was associated with unfavorable UC behavior. Anti-OmpC positivity was associated with a lack of response to anti-TNF therapy at 1 year (odds ratio 0.14 [95% CI 0.03-0.60], pcorr = 0.04) and increased likelihood of therapy discontinuation (HR 2.2 [95% CI 1.1-4.7], P = 0.03). CONCLUSION: Extensive colitis is associated with unfavorable disease course in UC. Anti-OmpC holds promise as a biomarker of anti-TNF therapy response in UC; however, prospective studies are required before it can be incorporated into routine clinical practice.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".