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Record W2343923995

Systemic lupus erythematosus in the pediatric North American Native population of British Columbia.

2006· article· en· W2343923995 on OpenAlexaffabout
Kristin Houghton, Jacqueline Page, David A. Cabral, Ross E. Petty, Lori B. Tucker

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsMedicineSystemic lupus erythematosusPopulationFamily historyConnective tissue diseaseDiseaseExact testInternal medicineRheumatologyPediatricsRetrospective cohort studyAutoimmune diseaseEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the estimated prevalence and the phenotype of pediatric systemic lupus erythematosus (SLE) in a North American Native population with other ethnic groups. METHODS: We performed a retrospective chart review of all patients with SLE currently followed at the single tertiary care pediatric rheumatology clinic in our province. Data collected included demographic characteristics, family history, classification criteria for SLE, laboratory tests at diagnosis, SLE Disease Activity Index (SLEDAI) at presentation, and Systemic Lupus International Collaborating Clinics (SLICC) damage index at 6 months. RESULTS: The prevalence of SLE in our pediatric Native population is 8.8 per 100,000 (n = 6) compared to 3.3 per 100,000 in the non-NAI population (n = 34) (p = 0.037, Fisher's exact test; OR 2.6, 95% CI 1.1-6.3). Family history of rheumatic disease is more common in our Native children (5/6, 83%) compared to non-Native children (5/34, 15%) (p = 0.002 Fisher's exact test; OR 29, 95% CI 2.8-303.3). The sample size is too small for reliable interpretation of disease phenotype, autoantibodies, disease activity, and disease damage measures. CONCLUSION: There is an increased prevalence of SLE and familial autoimmunity among Native children in our population. Public health measures to screen children at risk may detect early disease and may reduce disease morbidity.

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 score0.016
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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

Citations44
Published2006
Admission routes2
Has abstractyes

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