Incidence of Inflammatory Bowel Disease in Children
Bibliographic record
Abstract
Background: Many of the patients with inflammatory bowel disease (IBD) are children and adolescents, and the incidence of pediatric IBD is increasing. However, understanding epidemiological trends is crucial for effective prevention and treatment and reducing the local and global burden of IBD. Little data exist regarding the incidence of IBD in the child population in the Kujawsko-Pomorskie Voivodeship. The aims of this study were to evaluate the incidence of IBD in the period 2011 - 2022 and to compare the data regarding three types of IBD, namely ulcerative colitis (UC), Crohn's disease (CD), and unclassified inflammatory bowel disease (IBD-U), from the first half, i.e. 2011 - 2016, to the second half, i.e. 2017 - 2022. Methods: This retrospective study analyzed the medical records of 118 IBD patients hospitalized at the Department of Pediatrics, Allergology and Gastroenterology from the central-northern part of Poland. Results: Of the 118 patients diagnosed with IBD, 48 (40.68%) had CD, 57 (48.31%) had UC, and 13 (11.01%) had IBD-U. Between 2011 and 2016, 48 new IBD patients were diagnosed, with a further 70 new cases added between 2017 and 2022, representing a significant increase over the period (P = 0.033). Also, a significant increase was seen for UC, i.e. rising from 19 new cases between 2011 and 2016, to 38 between 2017 and 2022 (P = 0.015). The increase in CD was not significant. Conclusion: The incidence of pediatric IBD in the central-northern district of Poland is lower than other countries, it nonetheless appears to be increasing, particularly in children with UC. The number of IBD diagnoses in children has increased by nearly 50% over the last 6 years.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".