Early Change in Fecal Calprotectin Predicts One‐Year Outcome in Children Newly Diagnosed With Ulcerative Colitis
Bibliographic record
Abstract
INTRODUCTION: While fecal calprotectin (FC) is used to assess disease activity in ulcerative colitis (UC) there are little data concerning the role of serial FC levels at diagnosis in predicting clinical course. We sought to determine whether FC at diagnosis or early change following therapy predicts clinical outcomes in pediatric UC.Methods: Children with newly diagnosed UC were treated with standardized regimens of mesalamine or corticosteroids (CS). CS tapering and escalation to additional therapy or colectomy were by protocol. Patients with baseline or week 4 or week 12 FC levels were included in the analysis. Our primary outcome was CS-free remission on mesalamine at week 52. We compared the prognostic value of a baseline FC as well as a change in FC by week 4 or week 12 in predicting clinical outcomes. RESULTS: The study included 352 children (113 initial mesalamine, 239 initial CS, mean age 12.6 years) with UC. At Week 52, 135 (38.3%), 84 (23.8%), and 19 (5.4%) children achieved CS-free remission, needed anti-tumor necrosis factor therapy or had colectomy respectively. Baseline FC was not associated with CS-free remission at week 52. However, both week 4 (odds ratio [OR] 0.95, 95% confidence interval [CI] 0.901.00) and week 12 FC levels (OR 0.91, 95% CI 0.87-0.96) were associated with outcomes, with the latter having a stronger association with CS-free remission. Patients with a >75% decrease by 12 weeks, had a 3-fold increased likelihood of CS-free remission at 1 year. DISCUSSION: Longitudinal changes in FC may predict 1 year outcomes better than values at diagnosis in children with a new diagnosis of 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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".