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Record W2036866121 · doi:10.3899/jrheum.071326

Association Between the Aggrecan Gene and Rheumatoid Arthritis

2008· article· en· W2036866121 on OpenAlexvenueno aff
Thais Barboza de Souza, Elisa Fabiane Mentz, Claiton Viegas Brenol, Ricardo Machado Xavier, João Carlos Tavares Brenol, José Artur Bogo Chies, Daniel Simon

Bibliographic record

VenueThe Journal of Rheumatology · 2008
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
Fundersnot available
KeywordsAggrecanRheumatoid arthritisMedicineAlleleVariable number tandem repeatPolymerase chain reactionPolymorphism (computer science)GeneticsMinisatelliteAllele frequencyArthritisImmunologyGeneMicrosatelliteBiologyPathologyOsteoarthritisArticular cartilage

Abstract

fetched live from OpenAlex

OBJECTIVE: Genetic and environmental factors seem to be involved in the onset of rheumatoid arthritis (RA). We analyzed whether a variable number of tandem repeats (VNTR) polymorphism in the aggrecan gene was associated to RA. METHODS: The study population comprised 170 European-derived Brazilian patients with diagnosis of RA. The control group comprised 148 European-derived Brazilian healthy blood donors. The aggrecan VNTR polymorphism was genotyped by DNA amplification by polymerase chain reaction, followed by electrophoresis in polyacrylamide gel. RESULTS: There was a statistically significant higher frequency of alleles of shorter length in the patient group compared to controls (p = 0.001), suggesting that individuals carrying short alleles are more likely to develop RA. There was no association between short alleles and clinical characteristics of RA. CONCLUSION: Our results provide evidence of an association between the aggrecan gene VNTR polymorphism and RA.

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 score0.003
Threshold uncertainty score0.009

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)0.0030.000

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 head0.016
GPT teacher head0.255
Teacher spread0.239 · 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

Citations4
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Rheumatology→Same topicRheumatoid Arthritis Research and Therapies→French-language works237,207→