The administrative prevalence and pharmacotherapy of chronic inflammatory bowel diseases, 2012–2020
Bibliographic record
Abstract
Ulcerative colitis (UC) and Crohn's disease (CD) are chronic inflammatory bowel diseases (IBD) with high prevalence and morbidity.The highest prevalence reported for UC is 0.51% in Norway; for MC it is 0.32% in Germany and Canada (1).The treatment of chronic IBD is based on immunomodulation and immunosuppression.Corticosteroids and long-term immunomodulatory drugs such as azathioprine and methotrexate are used, as are biologic agents and 5-aminosalicylates (2). MethodsThis study was based on nationwide outpatient drug prescription data in accordance with § 300 paragraph 2 of the German Social Code V (SGB V) and outpatient claims data in accordance with § 295 of the SGB V.The annual administrative prevalence (2012-2020) was calculated based on the percentage of CD and UC patients within the population of statutory health-insured individuals residing in Germany.Individuals were considered to have one of the diseases in question if they had a confirmed diagnosis in at least two quarters of 1 year (ICD-10-GM code for CD: K50; for UC: K51).The annual total number of insured persons was taken from the KM6 statistics of the German Federal Ministry of Health (rhttps://www.bundesgesundheitsministerium.de/themen/krankenversicherung/zahlen-und-fakten-zur-krankenversicherung/mitgliederund-versicherte.html).The prescription prevalence of the drugs was determined per year based on the number of patients with at least one prescription per 1000 affected individuals with diagnosed disease.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".