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Record W7153055960 · doi:10.15537/1658-3175.5963

Systemic lupus erythematosus in Arab children. Differences and similarities with different ethnicities

2014· article· en· W7153055960 on OpenAlexaboutno aff
Ashwaq A. Ale’ed, Sulaiman M Al-Mayouf

Bibliographic record

VenueSaudi Medical Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupLupus erythematosusImmunopathologyConnective tissue diseaseNegroidEpidemiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe systemic lupus erythematosus (SLE) features among Arab children, and compare with cohorts from different ethnicities. METHODS: This retrospective analysis of all published English literature on SLE in Arab children was conducted in March 2013. The percentage and frequencies of the clinical and laboratory features were collected, and compared between different Arab countries as well as Caucasian and South East Asian cohorts. RESULTS: A total of 560 children from 5 Arab cohorts with an average age at diagnosis of 10 years; 7.7% of patients were diagnosed before the age of 5 years. Familial SLE was frequent. Most patients had major organ involvement. Renal involvement was diagnosed in 80%, while neuropsychiatric manifestations were seen in 30%. Immunosuppressive agents were commonly used. Beta cell depletion was recently introduced for refractory cases. The outcome of the disease could not be determined from the available data. However, 145 out of 300 patients had disease damage (52.6% Saudi and 43.9% Egyptian) with mean of 1.3 for Saudi and 0.93 for Egyptian. Forty patients died during the disease course due to infections and severe organ disease. CONCLUSION: Systemic lupus erythematosus is common in Arab children, particularly familial SLE. The manifestations observed in Arabs are comparable with previous reports. However, there is a noticeable difference in the damage accrual and mortality rate between Arab and Canadian studies, which might reflect the disease severity.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.269
Teacher spread0.253 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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