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Record W1796319742 · doi:10.3109/08830185.2015.1065828

Genes and Environment as Predisposing Factors in Autoimmunity: Acceleration of Spontaneous Thyroiditis by Dietary Iodide in NOD.H2<sup>h4</sup>Mice

2015· review· en· W1796319742 on OpenAlexafffund
Panayota Kolypetri, Justin J. King, Mani Larijani, George Carayanniotis

Bibliographic record

VenueInternational Reviews of Immunology · 2015
Typereview
Languageen
FieldMedicine
TopicThyroid Disorders and Treatments
Canadian institutionsMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsNodAutoimmunityThyroiditisAutoimmune thyroiditisImmunologyDiseaseAutoimmune diseaseMedicineGeneBiologyGeneticsInternal medicineImmune systemAntibody

Abstract

fetched live from OpenAlex

In the field of autoimmune thyroiditis, NOD.H2(h4) mice have attracted significant and increasing attention since they not only develop spontaneous disease but they present thyroiditis with accelerated incidence and severity if they ingest iodide through their drinking water. This animal model highlights the interplay between genetic and dietary factors in the triggering of autoimmune disease and offers new opportunities to study immunoregulatory parameters influenced by both genes and environment. Here, we review experimental findings with this mouse model of thyroiditis.

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)0.000
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.048
GPT teacher head0.329
Teacher spread0.281 · 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
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

Citations22
Published2015
Admission routes2
Has abstractyes

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