Predicting Outcomes in Pediatric Ulcerative Colitis for Management Optimization: Systematic Review and Consensus Statements From the Pediatric Inflammatory Bowel Disease–Ahead Program
Bibliographic record
Abstract
BACKGROUND & AIMS: A better understanding of prognostic factors in ulcerative colitis (UC) could improve patient management and reduce complications. We aimed to identify evidence-based predictors for outcomes in pediatric UC, which may be used to optimize treatment algorithms. METHODS: Potential outcomes worthy of prediction in UC were determined by surveying 202 experts in pediatric UC. A systematic review of the literature, with selected meta-analysis, was performed to identify studies that investigated predictors for these outcomes. Multiple national and international meetings were held to reach consensus on evidence-based statements. RESULTS: Consensus was reached on 31 statements regarding predictors of colectomy, acute severe colitis (ASC), chronically active pediatric UC, cancer and mortality. At diagnosis, disease extent (6 studies, N = 627; P = .035), Pediatric Ulcerative Colitis Activity Index score (4 studies, n = 318; P < .001), hemoglobin, hematocrit, and albumin may predict colectomy. In addition, family history of UC (2 studies, n = 557; P = .0004), extraintestinal manifestations (4 studies, n = 526; P = .048), and disease extension over time may predict colectomy, whereas primary sclerosing cholangitis (PSC) may be protective. Acute severe colitis may be predicted by disease severity at onset and hypoalbuminemia. Higher Pediatric Ulcerative Colitis Activity Index score and C-reactive protein on days 3 and 5 of hospital admission predict failure of intravenous steroids. Risk factors for malignancy included concomitant diagnosis of primary sclerosing cholangitis, longstanding colitis (>10 years), male sex, and younger age at diagnosis. CONCLUSIONS: These evidence-based consensus statements offer predictions to be considered for a personalized medicine approach in treating pediatric 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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".