MétaCan
Menu
Back to cohort
Record W1981748239 · doi:10.1002/ibd.21511

Challenges associated with identifying the environmental determinants of the inflammatory bowel diseases

2010· review· en· W1981748239 on OpenAlexafffund
Natalie A. Molodecky, Remo Panaccione, Subrata Ghosh, Herman W. Barkema, Gilaad G. Kaplan

Bibliographic record

VenueInflammatory Bowel Diseases · 2010
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health ResearchAlberta InnovatesAlberta Heritage Foundation for Medical Research
KeywordsInflammatory bowel diseaseInflammatory Bowel DiseasesMedicineUlcerative colitisDiseaseEnvironmental epidemiologyEnvironmental healthIdentification (biology)Observational studyPathologyBiologyEcology

Abstract

fetched live from OpenAlex

In the last several years there has been an explosion in the discovery of inflammatory bowel disease (IBD) susceptibility genes; however, similar advances in identifying and defining environmental risk factors associated with IBD have lagged behind. Moreover, many studies that have explored the same or similar environmental risk factors of IBD have demonstrated disparate results and come to conflicting conclusions. In order for the field to move forward, it is important to understand and resolve why these differences exist. This significant heterogeneity has blurred the identification of the fundamental environmental determinants of IBD. The purpose of this review article is to explore the factors that have likely contributed to the heterogeneity among observational studies of environmental risk factors in IBD. In doing so, it is hoped that methodological standardization may lead to consistent environmental associations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0030.004
Science and technology studies0.0010.003
Scholarly communication0.0030.005
Open science0.0020.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.002

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 head0.019
GPT teacher head0.264
Teacher spread0.245 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations111
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueInflammatory Bowel DiseasesSame topicInflammatory Bowel DiseaseFrench-language works237,207