Early endoscopic, laboratory and clinical predictors of poor disease course in paediatric ulcerative colitis
Bibliographic record
Abstract
OBJECTIVE: Data to support treatment algorithms in ambulatory paediatric UC are scarce. We aimed to explore the 1 year outcome in an inception cohort of paediatric UC patients and to identify early predictors of good outcome that might serve as short term treatment targets. DESIGN: A chart review of 115 children with new onset UC was performed (age 11 ± 4.1 years; 58 (50%) males; 86 (75%) extensive colitis; 70 (61%) moderate-severe disease; 63 (55%) received steroids at baseline). We assessed the Paediatric Ulcerative Colitis Activity Index (PUCAI) and laboratory variables at the time of diagnosis and at 3 months, and endoscopy at diagnosis. RESULTS: The 3 month PUCAI was the strongest predictor of 1 year sustained steroid free remission (SSFR) (area under the receiver operating characteristic curve (AUROC)=0.7 (95% CI 0.6 to 0.8) and colectomy by 2 years (AUROC=0.75 (0.6 to 0.89)). SSFR was achieved in 9/54 (17%) children who had active disease (PUCAI ≥ 10) at 3 months (negative predictive value (NPV)=83%) and by 4/46 (8.6%) of those with a PUCAI score >10; (NPV=91%, positive predictive value=52%; p<0.001), implying that PUCAI >10 at 3 months has a probability of 9% for achieving SSFR versus 48% with a PUCAI value of ≤10. None of the variables at baseline was predictive of SSFR or colectomy (endoscopic severity, disease extent, age, PUCAI or C reactive protein/erythrocyte sedimentation rate/albumin/haemoglobin; all AUROC<0.6, p>0.05) but baseline PUCAI predicted subsequent acute severe colitis and the need for salvage medical therapy. CONCLUSIONS: Completeness of the early response appears more important than baseline UC severity for predicting outcome in children, and supports using PUCAI<10 as a feasible treatment goal. Our data suggest that treatment escalation should be considered with a PUCAI value of ≥ 10 at 3 months.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".