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Record W2520120283 · doi:10.1136/lupus-2016-000179.103

CE-24 Comparison of systemic lupus erythematosus in 3 different asian ethnic groups: results from the 1000 canadian faces of lupus cohort

2016· article· en· W2520120283 on OpenAlexaffabout
Mai P. Nguyen, Paul R. Fortin, Earl D. Silverman, Janet Pope, Gaëlle Chédeville, Adam M. Huber, Sasha Bernatsky, Ann E. Clarke, Christian A. Pineau, Marie Hudson, Hector Arbillaga, Lori B. Tucker, Deborah M. Levy, C. Douglas Smith, Carol Hitchon, Michel Zummer, Christine Peschken

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of OttawaUniversity of British ColumbiaUniversity of CalgaryMcGill UniversityDalhousie UniversityUniversity of TorontoUniversité de MontréalUniversité LavalWestern UniversityIzaak Walton Killam Health CentreUniversity of Manitoba
Fundersnot available
KeywordsMedicineCohortSystemic lupus erythematosusEthnic groupInternal medicineDemographyUnivariate analysisLupus erythematosusCohort studyMultivariate analysisDiseaseImmunologyAntibody

Abstract

fetched live from OpenAlex

Background Systemic lupus erythematosus (SLE) is more prevalent and severe in non-Caucasians including Asians. However, Asian ethnicity includes broad geographic, cultural, and genetic diversity. There is limited data examining SLE among North American Asian ethnicities. We describe SLE in 3 Asian subgroups from a large SLE cohort. Materials and methods The 1000 Faces of Lupus is a multicenter Canadian cohort of over 2000 patients. Sociodemographics, ACR classification criteria (ACRc), autoantibodies, disease activity scores (SLEDAI), Systemic Lupus International Collaborating Clinics damage index (SDI) scores, and treatments are collected using standardised tools. Ethnicity was self-reported. Asian subgroups were divided by origin country into East Asian (EA), Southeast Asian (SEA), South Asian (SA) and Central Asian (CA). Baseline data for Asians and Caucasians were abstracted and cross-sectional univariate analyses including t-tests, one-way ANOVA, and chi-square tests were performed. Results There were 334 Asians (EA = 176, SEA = 78, SA = 78, CA = 2), and 1275 Caucasians. CA were excluded. Mean Asian onset age was younger (EA = 23 ± 13 years; SEA = 21±10 years; SA = 20 ± 11 years, Caucasian 33 ± 15 years, p < 0.001), but this was due to very frequent childhood onset in Asians (EA = 49%; SEA = 51%; SA = 61%) compared to Caucasians (17%, p < 0.001) (Figure 1). Over 40% of Asians were immigrants, and a higher proportion were males (EA = 15%; SEA = 16%; SA = 19%) compared to Caucasians (10%, p = 0.008). More Asians (90%) completed high school compared to Caucasians (83%, p = 0.007). Income was similar between all Asian subgroups and Caucasians. ACRc and SLEDAI scores were not different, but nephritis was more frequent in all Asians: (EA = 57%; SEA = 63%; SA = 51%) compared to Caucasians (33%, p < 0.001). Asians were more frequently (ever) seropositive: (dsDNA+: EA = 62%; SEA = 63%; SA = 78%; Caucasians 52%, p < 0.001). (antiSm+: EA = 31%; SEA = 50%; SA = 30%; p = 0.01, Caucasian 19%, p < 0.001). (antiRNP+: EA = 20%; SEA = 32%; SA = 22%; p = 0.03, Caucasians 16%, p < 0.001). Treatment with prednisone (EA = 55%; SEA = 67%; SA = 65%), cyclophosphamide (EA = 13%; SEA = 21%; SA = 20%), and mycophenolate (EA = 15%; SEA = 19%; SA = 9%) was more frequent in Asians compared to Caucasians (40%, 10%, 8%, respectively, p < 0.001 for all) likely reflecting renal disease. Mean disease duration in Asians was 8 years but most had no damage (SDI = 0, EA = 66%; SEA = 64%; SA = 79%) compared to Caucasians (47%, p < 0.001). Conclusions In this analysis comparing Asian ethnic subgroups, we found only subtle differences between EA, SEA, and SA with SLE; as expected disease appeared more severe than in Caucasians. However, a strikingly high proportion of all Asians had onset in childhood. Along with the high proportion who were new Canadians, this suggests the potential for a growing burden of SLE in this population. Future studies of outcomes and optimal treatments are indicated. Acknowledgements Presented on behalf of Canadian Network for Improved Outcomes for Systemic Lupus Erythematous (CaNIOS) 1000 Faces Investigators.

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)0.002
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 score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.311
Teacher spread0.269 · 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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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