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Record W3164141155 · doi:10.1002/art.41876

Neuropsychiatric Events in Systemic Lupus Erythematosus: Predictors of Occurrence and Resolution in a Longitudinal Analysis of an International Inception Cohort

2021· article· en· W3164141155 on OpenAlexafffund
John G. Hanly, Caroline Gordon, Sang‐Cheol Bae, Juanita Romero‐Díaz, Jorge Sánchez‐Guerrero, Sasha Bernatsky, Ann E. Clarke, Daniel J. Wallace, David Isenberg, Anisur Rahman, Joan T. Merrill, Paul R. Fortin, Dafna D. Gladman, Murray B. Urowitz, Ian N Bruce, Michelle Petri, Ellen M. Ginzler, M.A. Dooley, Rosalind Ramsey‐Goldman, Susan Manzi, Andreas Jönsen, Graciela S. Alarcón, Ronald van Vollenhoven, Cynthia Aranow, Meggan Mackay, Guillermo Ruiz‐Irastorza, S. Sam Lim, Murat İnanç, Kenneth Kalunian, Søren Jacobsen, Christine Peschken, Diane L. Kamen, Anca Askanase, Vernon T. Farewell

Bibliographic record

VenueArthritis & Rheumatology · 2021
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsCentre hospitalier de l'Université LavalUniversity of ManitobaToronto Western HospitalCentre hospitalier universitaire de QuébecUniversity of CalgaryMcGill UniversityQueen Elizabeth II Health Sciences CentreUniversity of TorontoDalhousie University
FundersNational Center for Research ResourcesNational Institutes of HealthNational Institute of Arthritis and Musculoskeletal and Skin DiseasesVersus ArthritisEusko JaurlaritzaUniversity College LondonNational Institute for Health and Care ResearchArthritis SocietyCanadian Institutes of Health ResearchLupus Research AllianceSandwell and West Birmingham Hospitals NHS TrustNational Center for Advancing Translational SciencesWellcome TrustGigtforeningenMcGill UniversityUniversity of CalgaryJohns Hopkins UniversityGlaxoSmithKlineBristol-Myers SquibbAstraZenecaEli Lilly and CompanyManchester Biomedical Research CentreUniversité Laval
KeywordsCohortMedicineCohort studyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine predictors of change in neuropsychiatric (NP) event status in a large, prospective, international inception cohort of patients with systemic lupus erythematosus (SLE). METHODS: Upon enrollment and annually thereafter, NP events attributed to SLE and non-SLE causes and physician-determined resolution were documented. Factors potentially associated with the onset and resolution of NP events were determined by time-to-event analysis using a multistate modeling structure. RESULTS: NP events occurred in 955 (52.3%) of 1,827 patients, and 593 (31.0%) of 1,910 unique events were attributed to SLE. For SLE-associated NP (SLE NP) events, multivariate analysis revealed a positive association with male sex (P = 0.028), concurrent non-SLE NP events excluding headache (P < 0.001), active SLE (P = 0.012), and glucocorticoid use (P = 0.008). There was a negative association with Asian race (P = 0.002), postsecondary education (P = 0.001), and treatment with immunosuppressive drugs (P = 0.019) or antimalarial drugs (P = 0.056). For non-SLE NP events excluding headache, there was a positive association with concurrent SLE NP events (P < 0.001) and a negative association with African race (P = 0.012) and Asian race (P < 0.001). NP events attributed to SLE had a higher resolution rate than non-SLE NP events, with the exception of headache, which had comparable resolution rates. For SLE NP events, multivariate analysis revealed that resolution was more common in patients of Asian race (P = 0.006) and for central/focal NP events (P < 0.001). For non-SLE NP events, resolution was more common in patients of African race (P = 0.017) and less common in patients who were older at SLE diagnosis (P < 0.001). CONCLUSION: In a large and long-term study of the occurrence and resolution of NP events in SLE, we identified subgroups with better and worse prognosis. The course of NP events differs greatly depending on their nature and attribution.

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)0.001
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 score0.047
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.288
Teacher spread0.275 · 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

Citations25
Published2021
Admission routes2
Has abstractyes

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