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Record W2125869507 · doi:10.3748/wjg.v16.i15.1828

Risk factors in familial forms of celiac disease

2010· review· en· W2125869507 on OpenAlexaffabout
Hugh James Freeman

Bibliographic record

VenueWorld Journal of Gastroenterology · 2010
Typereview
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDiseaseAsymptomaticMedicineFirst-degree relativesRisk factorHuman leukocyte antigenLymphomaImmunologyFamily historyInternal medicineAntigen

Abstract

fetched live from OpenAlex

Celiac disease has been reported in up to 2% of some European populations. A similar risk has been identified in the America and Australia where immigration of Europeans has occurred. Moreover, an increasing number of celiac disease patients are being identified in many Asian countries, including China and India. Finally, celiac disease has also been detected in Asian immigrants and their descendants to other countries, such as Canada. Within these so-called "general" celiac populations, however, there are specific high risk groups that have an even higher prevalence of celiac disease. Indeed, the single most important risk factor for celiac disease is having a first-degree relative with already-defined celiac disease, particularly a sibling. A rate up to 20% or more has been noted. Risk is even greater if a specific family has 2 siblings affected, particularly if a male carries the human leukocyte antigen-DQ2. Both structural changes in the small bowel architecture occur along with functional changes in permeability, even in asymptomatic first-degree relatives. Even if celiac disease is not evident, the risk of other autoimmune disorders seems significantly increased in first-degree relatives as well as intestinal lymphoma. Identification of celiac disease is important since recent long-term studies have shown that the mortality of celiac disease is increased, if it is unrecognized and untreated.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.957
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.023
GPT teacher head0.330
Teacher spread0.307 · 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 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

Citations45
Published2010
Admission routes2
Has abstractyes

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