Fecal osteoprotegerin may guide the introduction of second-line therapy in hospitalized children with ulcerative colitis
Bibliographic record
Abstract
BACKGROUND: Osteoprotegerin (OPG) is increased in inflamed colonic mucosa and has a role in immune regulation and apoptosis resistance. Fecal OPG may be useful in predicting corticosteroid resistance in hospitalized children with severe ulcerative colitis (UC). We aimed to determine whether fecal OPG predicts the need for second-line therapies in children hospitalized for UC. METHODS: We included 83 children with UC admitted for intravenous corticosteroid treatment. Children were classified as responders/nonresponders based on the need for therapy escalation. Fecal OPG results were compared with those of four other fecal markers. RESULTS: Of the enrolled children, seven had day 1 samples only, 53 children had day 3 samples only, and 23 had both. Twenty-two children failed corticosteroid therapy and required infliximab (n = 20) or colectomy (n = 2). On the third treatment day the median fecal OPG levels were significantly higher in the nonresponders group compared with the responders: 77 pmol/L (interquartile range [IQR] 27-137) versus 13 pmol/L (3-109); P = 0.007. The best day 3 fecal OPG cutoff to predict second-line therapy was >50 pmol/L with a sensitivity of 71% and specificity of 69% (area under the receiver operator curve [ROC] of 0.70%-95% confidence interval [CI] 0.57-0.82). Fecal OPG was superior to day 3 fecal calprotectin, lactoferrin, and S100A12 as a predictor of corticosteroid nonresponse, but equivalent to the less commonly used M2-pyruvate kinase. CONCLUSIONS: Day 3 fecal OPG may guide the decision to institute second-line therapy in children with severe UC. The role of OPG in the inflammatory response in pediatric UC deserves further study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".