Pediatric Chronic Inflammatory Bowel Disease in a German Statutory Health INSURANCE—Incidence Rates From 2009 to 2012
Bibliographic record
Abstract
OBJECTIVE: The incidence of pediatric inflammatory bowel disease (PIBD) varies over time and geographic region. We attempted to generate incidence rates form German health insurance data. METHODS: We used health care data for 2009-2015 provided by BARMER, a major statutory health insurance company in Germany, insuring approximately 8% of the pediatric population. We applied a Canadian case definition for PIBD based on International Classification of Diseases coding, documentation of (ileo)colonoscopy and the number of PIBD related visits, without external validation for Germany. An internal validation of the specificity of the diagnosis by checking whether the identified incident cases had also prescriptions of PIPD specific drugs was performed. RESULTS: In 2012, 187 pediatric patients were newly diagnosed, accounting for an overall PIBD incidence of 17.41 (95% CI 15.08-20.10) per 100,000 insured children and adolescents from 0 to 17.9 years per year compared with 13.65/100,000 (95% CI 11.63-16.01) in 2009. The age-specific incidence showed a steep increase as of the age of 7 years. The PIBD prevalence in 2012 was 66.29/100,000. CONCLUSIONS: In conclusion, the incidence of PIBD in 0 to 17.9-year-olds in Germany with health BARMER health insurance in 2012 is among the highest reported in the literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".