Clinical factors associated with severity in patients with inflammatory bowel disease in Brazil based on 2-year national registry data from GEDIIB
Bibliographic record
Abstract
The Brazilian Organization for Crohn's Disease and Colitis (GEDIIB) established a national registry of inflammatory bowel disease (IBD). The aim of the study was to identify clinical factors associated with disease severity in IBD patients in Brazil. A population-based risk model aimed at stratifying the severity of IBD based on previous hospitalization, use of biologics, and need for surgery for ulcerative colitis (UC) and Crohn's Disease (CD) and on previous complications for CD. A total of 1179 patients (34.4 ± 14.7y; females 59%) were included: 46.6% with UC, 44.2% with CD, and 0.9% with unclassified IBD (IBD-U). The time from the beginning of the symptoms to diagnosis was 3.85y. In CD, 41.2% of patients presented with ileocolic disease, 32% inflammatory behavior, and 15.5% perianal disease. In UC, 46.3% presented with extensive colitis. Regarding treatment, 68.1%, 67%, and 47.6% received biological therapy, salicylates and immunosuppressors, respectively. Severe disease was associated with the presence of extensive colitis, EIM, male, comorbidities, and familial history of colorectal cancer in patients with UC. The presence of Montreal B2 and B3 behaviors, colonic location, and EIM were associated with CD severity. In conclusion, disease severity was associated with younger age, greater disease extent, and the presence of rheumatic EIM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".