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Record W4285009614 · doi:10.1093/ibd/izac124

The Current State of Care for Black and Hispanic Inflammatory Bowel Disease Patients

2022· review· en· W4285009614 on OpenAlexaff
Julia J. Liu, Bincy Abraham, Paula Adamson, Edward L. Barnes, Kelly A. Brister, Oriana M. Damas, Sarah C. Glover, Kimberly Hooks, Ana Ingram, Gilaad G. Kaplan, Edward V. Loftus, Dermot McGovern, Melodie Narain-Blackwell, Florence-Damilola Odufalu, Sandra Quezada, Vonda Reeves, Bo Shen, Thaddeus S. Stappenbeck, Latonia Ward

Bibliographic record

VenueInflammatory Bowel Diseases · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammatory Bowel Disease
Canadian institutionsUniversity of Calgary
FundersJanssen BiotechNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthTakeda FoundationAbbVieBristol-Myers Squibb
KeywordsInflammatory bowel diseaseUlcerative colitisMedicineDiseaseIncidence (geometry)EpidemiologyCrohn's diseaseHealth careClinical trialIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Research on the care of inflammatory bowel disease (IBD) patients has been primarily in populations of European ancestry. However, the incidence of IBD, which comprises Crohn's disease and ulcerative colitis, is increasing in different populations around the world. In this comprehensive review, we examine the epidemiology, clinical presentations, disease phenotypes, treatment outcomes, social determinants of health, and genetic and environmental factors in the pathogenesis of IBD in Black and Hispanic patients in the United States. To improve health equity of underserved minorities with IBD, we identified the following priority areas: access to care, accurate assessment of treatment outcomes, incorporation of Black and Hispanic patients in therapeutic clinical trials, and investigation of environmental factors that lead to the increase in disease incidence.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.012
GPT teacher head0.270
Teacher spread0.258 · 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

Citations30
Published2022
Admission routes1
Has abstractyes

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