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Record W4405016655 · doi:10.3238/arztebl.m2024.0138

The administrative prevalence and pharmacotherapy of chronic inflammatory bowel diseases, 2012–2020

2024· letter· de· W4405016655 on OpenAlexfundaboutno aff
Karsten H. Weylandt, Adelheid Jung, Christoph Schmöcker, Daniel C. Baumgart, Felicia Turowski, Claudia Kohring, Kerstin Klimke, Manas K. Akmatov, Jörg Bätzing, Dawid Pieper, Jakob Holstiege

Bibliographic record

VenueDeutsches Ärzteblatt international · 2024
Typeletter
Languagede
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
FundersUniversität PotsdamUniversity of Alberta
KeywordsPharmacotherapyInflammatory Bowel DiseasesMedicineInflammatory bowel diseaseIntensive care medicineInternal medicineDisease

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.278
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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