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Record W2982675373 · doi:10.3748/wjg.v25.i40.6145

Ethnic differences in inflammatory bowel disease: Results from the United Kingdom inception epidemiology study

2019· article· en· W2982675373 on OpenAlexaboutno aff
Ravi Misra, Jimmy K. Limdi, Rachel Cooney, Samia Sakuma, Matthew Brookes, Edward Fogden, Sanjeev Pattni, Naveen Sharma, Tariq Iqbal, Pia Munkholm, Johan Burisch, Naila Arebi

Bibliographic record

VenueWorld Journal of Gastroenterology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsInflammatory bowel diseaseEpidemiologyEthnic groupMedicineCrohn's diseaseUlcerative colitisInternal medicineDiseaseGastroenterologyPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The current epidemiology of inflammatory bowel disease (IBD) in the multi-ethnic United Kingdom is unknown. The last incidence study in the United Kingdom was carried out over 20 years ago. AIM: To describe the incidence and phenotype of IBD and distribution within ethnic groups. METHODS: Adult patients (> 16 years) with newly diagnosed IBD (fulfilling Copenhagen diagnostic criteria) were prospectively recruited over one year in 5 urban catchment areas with high South Asian population. Patient demographics, ethnic codes, disease phenotype (Montreal classification), disease activity and treatment within 3 months of diagnosis were recorded onto the Epicom database. RESULTS: = 0.031). There was no significant difference in time to diagnosis, disease activity and treatment. CONCLUSION: This is the only prospective study to report the incidence of IBD in an ethnically diverse United Kingdom population. The Indian ethnic group showed the highest age-adjusted incidence of UC (20.5/100000). Further studies on dietary, microbial and metabolic factors that might explain these findings in UC are underway.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.646

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.288
Teacher spread0.255 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations40
Published2019
Admission routes1
Has abstractyes

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