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Agreement between local and central anti-synthetase antibodies detection: results from the Classification Criteria of Anti-Synthetase Syndrome project biobank

2024· article· en· W4392804783 on OpenAlexaff
Aravinthan Loganathan, Giovanni Zanframundo, Akira Yoshida, Sara Faghihi-Kashani, Iazsmin Bauer Ventura, Eduardo Dourado, F. Bozán, Gianluca Sambataro, Yasuhiko Yamano, Sharon Sangmee Bae, Darosa Lim, Angela Ceribelli, Natasa Isailovic, Carlo Selmi, Noreen Fertig, Elena Bravi, Yuko Kaneko, André Pinto Saraiva, Vega Jovaní, Javier Bachiller‐Corral, José M. Cifrián, Antonio Mera, Siamak Moghadam‐Kia, Verónica Wolff, Julien Campagne, Alain Meyer, Margherita Giannini, Konstantinos Triantafyllias, Johannes Knitza, Latika Gupta, Yair Molad, Florenzo Iannone, Ilaria Cavazzana, Matteo Piga, Giacomo De Luca, Sarah Tansley, Emanuele Bozzalla Cassione, Francesco Bonella, Tamera J. Corte, Tracy J. Doyle, David Fiorentino, M. Á. González-Gay, Marie Hudson, Masataka Kuwana, Ingrid E. Lundberg, Andrew L. Mammen, Neil McHugh, Fredrick W. Miller, Carlomaurizio Montecucco, Chester V. Oddis, Jorge Rojas‐Serrano, Jens Schmidt, Carlo Alberto Scirè, Albert Selva-O’Callaghan, Victoria P. Werth, Claudia Alpini, Sara Bozzinì, Lorenzo Cavagna, Rohit Aggarwal

Bibliographic record

VenueClinical and Experimental Rheumatology · 2024
Typearticle
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiobankMedicineAntibodyComputational biologyImmunologyBioinformaticsBiology

Abstract

fetched live from OpenAlex

OBJECTIVES: The CLASS (Classification Criteria of Anti-Synthetase Syndrome) project is a large international multicentre study that aims to create the first data-driven anti-synthetase syndrome (ASSD) classification criteria. Identifying anti-aminoacyl tRNA synthetase antibodies (anti-ARS) is crucial for diagnosis, and several commercial immunoassays are now available for this purpose. However, using these assays risks yielding false-positive or false-negative results, potentially leading to misdiagnosis. The established reference standard for detecting anti-ARS is immunoprecipitation (IP), typically employed in research rather than routine autoantibody testing. We gathered samples from participating centers and results from local anti-ARS testing. As an "ad-interim" study within the CLASS project, we aimed to assess how local immunoassays perform in real-world settings compared to our central definition of anti-ARS positivity. METHODS: We collected 787 serum samples from participating centres for the CLASS project and their local anti-ARS test results. These samples underwent initial central testing using RNA-IP. Following this, the specificity of ARS was reconfirmed centrally through ELISA, line-blot assay (LIA), and, in cases of conflicting results, protein-IP. The sensitivity, specificity, positive likelihood ratio and positive and negative predictive values were evaluated. We also calculated the inter-rater agreement between central and local results using a weighted κ co-efficient. RESULTS: Our analysis demonstrates that local, real-world detection of anti-Jo1 is reliable with high sensitivity and specificity with a very good level of agreement with our central definition of anti-Jo1 antibody positivity. However, the agreement between local immunoassay and central determination of anti-non-Jo1 antibodies varied, especially among results obtained using local LIA, ELISA and "other" methods. CONCLUSIONS: Our study evaluates the performance of real-world identification of anti-synthetase antibodies in a large cohort of multi-national patients with ASSD and controls. Our analysis reinforces the reliability of real-world anti-Jo1 detection methods. In contrast, challenges persist for anti-non-Jo1 identification, particularly anti-PL7 and rarer antibodies such as anti-OJ/KS. Clinicians should exercise caution when interpreting anti-synthetase antibodies, especially when commercial immunoassays test positive for non-anti-Jo1 antibodies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.088
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.362
Teacher spread0.308 · 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 teacher head, 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

Citations18
Published2024
Admission routes1
Has abstractyes

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