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Record W7116329787 · doi:10.1186/s13075-025-03717-2

Novel insights into the biology of childhood arthritis- lessons learned from the synovium

2025· article· en· W7116329787 on OpenAlexaff
Chrissy Bolton, Margaret H. Chang

Bibliographic record

VenueArthritis Research & Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicAutoimmune and Inflammatory Disorders Research
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsArthritisContext (archaeology)Inflammatory arthritisRheumatologySynovial membraneDiseasePresentation (obstetrics)Complex disease

Abstract

fetched live from OpenAlex

Chronic inflammatory arthritis of childhood, known as juvenile idiopathic arthritis (JIA), exhibits distinct and shared features with adult-onset disease. Recent advances in minimally invasive synovial biopsy, high resolution imaging and sequencing technologies have enabled detailed characterisation of the inflamed synovium in JIA, facilitating insights into the underlying immunopathology. In this review we draw on these findings to consider how the developing immunological and structural context of the joint impacts the presentation and consequence of inflammatory joint disease in children and young adults.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0010.001

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 head0.069
GPT teacher head0.381
Teacher spread0.312 · 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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