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Record W2117259188 · doi:10.1177/0961203306076220

Sm antibodies increase risk of death in systemic lupus erythematosus

2007· article· en· W2117259188 on OpenAlexaffabout
Carol Hitchon, Christine Peschken

Bibliographic record

VenueLupus · 2007
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineSystemic lupus erythematosusAutoantibodyEthnic groupInternal medicineExtractable nuclear antigensSocioeconomic statusLupus erythematosusImmunologyCohortRetrospective cohort studyAntibodyDiseaseAnti-nuclear antibodyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

The importance of ethnicity, socioeconomic status (SES), and autoantibodies as prognostic indicators in lupus were evaluated in a Canadian cohort. A retrospective review of 330 lupus patients identified demographic features including age and self reported ethnicity, SES, lupus features, antibodies to extractable nuclear antigens (ENAs), organ damage (SDI score), and mortality. ENA (Sm, RNP, Ro, La) associations with lupus features, predictors of final visit SDI score and the contributions of ethnicity, autoantibodies and SES on overall mortality were determined. Three ethnic groups [Caucasians (C), Asian-Orientals (AO), Native American First Nations (FN)] differed in disease severity and SES. FN and AO patients had similarly severe lupus, developing lupus at an earlier age, with more renal and neurological involvement, greater SDI scores at last visit, and more frequently had Sm or RNP antibodies than C. FN had the highest mortality and lowest SES. Sm and RNP antibodies were associated with renal and neurologic involvement. RNP, education and duration of follow-up predicted SDI score. Sm increased risk of death. In conclusion, RNP and lower SES are associated with lupus related organ damage and the presence of Sm is a predictor of mortality in lupus, independent of ethnicity, renal involvement or socioeconomic status.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.020
GPT teacher head0.302
Teacher spread0.282 · 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.

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

Citations27
Published2007
Admission routes2
Has abstractyes

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