Interleukin-6 is associated with steroid resistance and reflects disease activity in severe pediatric ulcerative colitis
Bibliographic record
Abstract
BACKGROUND AND AIM: Approximately one third of patients with acute severe ulcerative colitis (ASC) will fail intravenous corticosteroids (IVCS). Predicting response to IVCS to initiate early salvage therapy remains challenging. The aim of this study was to evaluate the role of serum inflammatory cytokines in ASC and determine their predictive utility with IVCS treatment failure. METHODS: This preplanned ancillary study, part of the prospective multicenter OSCI study, evaluated pediatric ASC in North America. Serum samples were obtained from 79 children admitted for ASC on the third day of IVCS treatment. Twenty-three (29%) patients required second-line therapy. ELISA-based cytokine arrays were used [TNF-α, IFN-γ, interleukin (IL)-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, IL-13, and IL-17], selected based on a systematic literature search. RESULTS: In univariate analysis, only IL-6 was significantly different between responders and non-responders (P=0.003). The risk for IVCS failure increased by 40% per each pg/mL increase in IL-6 level. Factor analysis found IL-6 to be associated with IL-17, suggesting involvement of the T-helper (TH)17 pathway. In a multivariate analysis, disease activity [judged by the Pediatric UC Activity Index (PUCAI)] assumed all the association with the treatment outcome while IL-6 was no longer significant (P=0.32; PUCAI score P<0.001). CONCLUSIONS: While IL-6 strongly predicted IVCS failure, it likely reflects disease activity and not direct interference with corticosteroid pathway. Nonetheless, IL-6 levels may have a role in predicting IVCS response in severe pediatric UC for treatment decision-making or potentially in medical intervention by virtue of anti-IL-6 antibodies in severe UC.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".