S3290 Clinical Variables as Predictors of Initial Relapse After Diagnosis of Pediatric Ulcerative Colitis
Bibliographic record
Abstract
INTRODUCTION: The aim of the study was to (1) determine if any clinical variables and laboratory values at presentation are predictors of relapse in pediatric UC; (2) To determine the differences in clinical variables and laboratory values for those who relapsed early, compared with who relapsed late. METHODS: Charts were review for all patients ages 1–21years with pediatric ulcerative colitis. Variables studied included: demographic parameters: age at diagnosis, gender, race, BMI percentiles, family history of IBD; presenting symptoms: gross blood in the stool, nocturnal stools, fever & extra-intestinal manifestations; phenotypic characteristics, using the Montreal classification; laboratory data: WBC count, hemoglobin, hematocrit, platelet count, ESR and CRP. Data was analyzed using χ2 test and Student’s t-test. A P-value < 0.05 was considered statistically significant. RESULTS: A total of 28 patients were included. 16 relapsed within first 2 years, >50% relapsed in the first 7months, defined as early relapse. The mean age for all the patients was 11.5 ± 4.8 years, 71.4% were males, and 96.4 % were Caucasians. CONCLUSION: According to our study 57% of the patients relapsed, comparable to other studies. No predictors of relapse between relapse vs. no relapse group and early vs. late relapse were found to be statistically significant.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".