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Record W4411442789 · doi:10.1016/j.gastha.2025.100732

Extraintestinal Symptoms of Pain Are Common in Patients With Eosinophilic Gastrointestinal Diseases

2025· article· en· W4411442789 on OpenAlexaboutno aff
Jennifer Dziwis, Xiangfeng Dai, Chelsea Anderson, Ellyn Kodroff, Mary Jo Strobel, Amy Zicarelli, Sarah A. O. Gray, Amanda Cordell, Girish Hiremath, Evan S. Dellon, Elizabeth T. Jensen

Bibliographic record

VenueGastro Hep Advances · 2025
Typearticle
Languageen
FieldMedicine
TopicEosinophilic Esophagitis
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesTakeda Pharmaceutical CompanyTakeda Pharmaceuticals U.S.A.Shire
KeywordsEosinophilicMedicineDermatologyInternal medicineGastroenterologyPathology

Abstract

fetched live from OpenAlex

Background and Aims: Extraintestinal symptoms are well-documented in systemic, inflammation-predominant conditions. Less is understood about extragastrointestinal symptoms among individuals with eosinophilic esophagitis (EoE) and non-EoE eosinophilic gastrointestinal diseases (EGIDs). We aimed to describe the differences in the frequency of patient-reported joint or leg pain and headache for EoE and non-EoE EGIDs individuals. Methods: Adult subjects and caregivers of children were recruited via the EGID Partners network and completed the Short-Form McGill Pain Questionnaire and Migraine Disability Assessment Test. T-tests were used to evaluate differences in extraintestinal pain symptomology by EGID type. Results: = .01). Conclusion: Patients with EGIDs may experience extraintestinal symptoms of pain. These symptoms may be more prominent in patients with non-EoE only EGIDs. Understanding of the underlying factors contributing to these symptoms is needed to guide mitigating approaches for these symptoms.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.004
GPT teacher head0.237
Teacher spread0.233 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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