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Record W2116817998 · doi:10.3899/jrheum.101261

High Levels of Anti-Cyclic Citrullinated Peptide Autoantibodies Are Associated with Co-occurrence of Pulmonary Diseases with Rheumatoid Arthritis

2011· article· en· W2116817998 on OpenAlexvenueno aff
Fleur Cohen‐Aubart, Bruno Crestani, P. Nicaise‐Roland, Florence Tubach, CAROLINE BOLLET, K. Dawidowicz, Emilie Quintin, Gilles Hayem, Élisabeth Palazzo, Olivier Meyer, Sylvie Chollet‐Martin, Philippe Dieudé

Bibliographic record

VenueThe Journal of Rheumatology · 2011
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRheumatoid arthritisAutoantibodyInternal medicineRheumatoid factorLogistic regressionPopulationLungImmunologyHigh-resolution computed tomographyArthritisRespiratory diseaseGastroenterologyAntibody

Abstract

fetched live from OpenAlex

OBJECTIVE: To investigate whether levels of anti-cyclic citrullinated peptide antibodies (anti-CCP2) in patients with rheumatoid arthritis (RA) are associated with the co-occurrence of lung diseases. METHODS: A total of 252 RA patients were included in a cross-sectional study. Pulmonary disease was confirmed by high-resolution chest computed tomography scan. Circulating anti-CCP2 were quantified using ELISA. Multivariate logistic regression was conducted to identify independent risk factors for lung disease. RESULTS: Male sex (OR 3.29, 95% CI 1.59-6.80) and high anti-CCP2 levels (OR 1.49, 95% CI 1.25-1.78) were identified as independent risk factors for lung disease in the RA population. CONCLUSION: High anti-CCP2 levels are associated with lung disease in the RA population.

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)0.003
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.258
Teacher spread0.235 · 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

Citations116
Published2011
Admission routes1
Has abstractyes

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