Analysis of Using the Total White Blood Cell Count to Define Severe New‐onset Ulcerative Colitis in Children
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to assess common laboratory tests in identifying severe ulcerative colitis in children at diagnosis. METHODS: A cohort of 427 children 4 to 17 years of age newly diagnosed with ulcerative colitis (UC) was prospectively enrolled. Boosted classification trees were used to characterize predictive ability of disease attributes based on clinical disease severity using Pediatric Ulcerative Colitis Activity Index (PUCAI), severe (65+) versus not severe (<65) and total Mayo score, severe (10-12) versus not severe (<10); mucosal disease by Mayo endoscopic subscore, severe (3) versus not severe (<3); and extensive disease versus not extensive (left-sided and proctosigmoiditis). RESULTS: Mean age was 12.7 years; 49.6% (n = 212) were girls, and 83% (n = 351) were Caucasian. Severe total Mayo score was present in 28% (n = 120), mean PUCAI score was 49.8 ± 20.1, and 33% (n = 142) had severe mucosal disease with extensive involvement in 82% (n = 353). Classification and regression trees identified white blood cell count, erythrocyte sedimentation rate, and platelet count (PLT) as the set of 3 best blood laboratory tests to predict disease extent and severity. For mucosal severity, albumin (Alb) replaced PLT. Classification models for PUCAI and total Mayo provided sensitivity of at least 0.65 using standard clinical cut-points with misclassification rates of approximately 30%. CONCLUSIONS: A combination of the white blood cell count, erythrocyte sedimentation rate, and either PLT or albumin is the best predictive subset of standard laboratory tests to identify severe from nonsevere clinical or mucosal disease at diagnosis in relation to objective clinical scores.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".