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Record W4380291357 · doi:10.1093/rheumatology/kead292

Damage measured by Damage Index for Antiphospholipid Syndrome (DIAPS) in antiphospholipid antibody-positive patients included in the APS ACTION registry

2023· article· en· W4380291357 on OpenAlexaff
Gustavo Guimarães Moreira Balbi, Yasaman Ahmadzadeh, Maria G. Tektonidou, Vittorio Pengo, Savino Sciascia, Amaia Ugarte, H. Michael Belmont, C. López-Pedrera, Paul R. Fortin, Denis Wahl, Maria Gerosa, Guilherme Ramires de Jesús, Lanlan Ji, Tatsuya Atsumi, Maria Efthymiou, D. Ware Branch, Cecilia Nalli, Esther Rodríguez-Almaraz, Michelle Petri, Ricard Cervera, Jason S. Knight, Bahar Artım-Esen, Rohan Willis, María Laura Bertolaccini, Hannah Cohen, Robert Roubey, Doruk Erkan, Danieli Andrade, JoAnn Vega, Guillermo Pons‐Estel, Bill Giannakopoulos, Steve Krilis, Roger A. Levy, Flávio Signorelli, Ann E. Clarke, Leslie Skeith, Zhouli Zhang, Chengde Yang, Hui Shi, Stéphane Zuily, Laura Andréoli, Anǵela Tincani, Cecilia Beatrice Chighizola, Pier Luigi Meroni, Chunyan Cheng, Giulia Pazzola, Silvia Grazietta Foddai, Massimo Radin, Stacy Davis, Olga Amengual, Imad Uthman, Maarten Limper, Philip de Groot, Guillermo Ruiz‐Irastorza, Ignasi Rodríguez‐Pintó, José Pardos‐Gea, M. Á. Aguirre, Ian Mackie, Giovanni Sanna, Yu Zuo, Rebecca Karp Leaf, Thomas L. Ortel, Nina Kello, Steven R. Levine, Jacob H. Rand, Medha Barbhaiya, Jane E. Salmon, Michael D. Lockshin, Ali A. Duarte Garcia

Bibliographic record

VenueLara D. Veeken · 2023
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversité Laval
FundersNational Center for Advancing Translational SciencesWeill Cornell Medical CollegeNational Institutes of Health
KeywordsMedicineAntiphospholipid syndromeInternal medicineGastroenterologyOdds ratioCohortThrombosis

Abstract

fetched live from OpenAlex

OBJECTIVES: Our primary objective was to quantify damage burden measured by Damage Index for Antiphospholipid Syndrome (DIAPS) in aPL-positive patients with or without a history of thrombosis in an international cohort (the APS ACTION cohort). Secondly, we aimed to identify clinical and laboratory characteristics associated with damage in aPL-positive patients. METHODS: In this cross-sectional study, we analysed the baseline damage in aPL-positive patients with or without APS classification. We excluded patients with other autoimmune diseases. We analysed the demographic, clinical and laboratory characteristics based on two subgroups: (i) thrombotic APS patients with high vs low damage; and (ii) non-thrombotic aPL-positive patients with vs without damage. RESULTS: Of the 826 aPL-positive patients included in the registry as of April 2020, 586 with no other systemic autoimmune diseases were included in the analysis (412 thrombotic and 174 non-thrombotic). In the thrombotic group, hyperlipidaemia (odds ratio [OR] 1.82; 95% CI 1.05, 3.15; adjusted P = 0.032), obesity (OR 2.14; 95% CI 1.23, 3.71; adjusted P = 0.007), aβ2GPI high titres (OR 2.33; 95% CI 1.36, 4.02; adjusted P = 0.002) and corticosteroid use (ever) (OR 3.73; 95% CI 1.80, 7.75; adjusted P < 0.001) were independently associated with high damage at baseline. In the non-thrombotic group, hypertension (OR 4.55; 95% CI 1.82, 11.35; adjusted P = 0.001) and hyperlipidaemia (OR 4.32; 95% CI 1.37, 13.65; adjusted P = 0.013) were independent predictors of damage at baseline; conversely, single aPL positivity was inversely correlated with damage (OR 0.24; 95% CI 0.075, 0.77; adjusted P = 0.016). CONCLUSIONS: DIAPS indicates substantial damage in aPL-positive patients in the APS ACTION cohort. Selected traditional cardiovascular risk factors, steroids use and specific aPL profiles may help to identify patients more prone to present with a higher damage burden.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.330
Teacher spread0.300 · 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

Citations17
Published2023
Admission routes1
Has abstractyes

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