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Genetic lesions in T‐cell tolerance and thresholds for autoimmunity

2005· review· en· W2121613610 on OpenAlexaff
Adrian Liston, Sylvie Lesage, Daniel H.D. Gray, Richard L. Boyd, Christopher C. Goodnow

Bibliographic record

VenueImmunological Reviews · 2005
Typereview
Languageen
FieldMedicine
TopicAdrenal Hormones and Disorders
Canadian institutionsUniversité de MontréalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsAutoimmunityBiologyImmunologyImmune toleranceSelf ToleranceGeneticsImmune system

Abstract

fetched live from OpenAlex

The cause of common organ-specific autoimmune diseases is poorly understood because of genetic and cellular complexity in humans and animals. Recent advances in the understanding of the mechanisms of the defects underlying autoimmune disease in autoimmune polyendocrinopathy syndrome type 1 and non-obese diabetic mice suggest that failures in central tolerance play a key role in predisposition towards organ-specific autoimmunity. The lessons from such rare monogenic autoimmune disorders and well-characterized polygenic traits demonstrate how subtle quantitative trait loci can result in large changes in the susceptibility to autoimmunity. These data allow us to propose a model relating efficiency of thymic deletion to T-cell tolerance and susceptibility to autoimmunity.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.132
GPT teacher head0.396
Teacher spread0.264 · 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

Citations68
Published2005
Admission routes1
Has abstractyes

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