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Record W2767076321 · doi:10.1177/0961203317739133

Systemic lupus erythematosus with and without a family history: a meta-analysis

2017· review· en· W2767076321 on OpenAlexaboutno aff
Linxia Chen, Zhenrui Shi, G. Tan, Yinuo Han, Zengqi Tang, Liangchun Wang

Bibliographic record

VenueLupus · 2017
Typereview
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisOdds ratioLupus nephritisInternal medicineWeb of scienceFamily historyLupus erythematosusNephritisConfidence intervalDermatologyImmunologyDisease

Abstract

fetched live from OpenAlex

Objective The objective of this paper is to investigate the association of clinical manifestations and laboratory parameters between familial systemic lupus erythematosus (SLE) and sporadic SLE. Methods All relevant literature was retrieved from the PubMed, EMBASE, Web of Science and China National Knowledge Infrastructure (CNKI) databases. The qualities of these studies were evaluated using a modified version of the Newcastle-Ottawa scale. The characteristics and clinical manifestations of involved individuals were extracted from each study. Pooled odds ratio (OR) was calculated using the random effects-method, and the heterogeneity between studies was quantified using the I 2 statistic. Results Of 330 studies identified by the search strategy, six were included in this review. In total, 733 cases were familial SLE and 1405 were sporadic SLE. Analysis revealed that photosensitivity, nephritis and thrombocytopenia were negatively associated with familial SLE, with OR (95% CI) values of 0.73 (0.60–0.89), 0.72 (0.59–0.88) and 0.75 (0.57–0.98), respectively. Conclusions Photosensitivity, thrombocytopenia and renal involvement could be more common in non-familial SLE, which should be further confirmed by well-designed studies with large populations.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.461
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0170.004
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.212
GPT teacher head0.383
Teacher spread0.171 · 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; both teacher heads agree on what is shown here.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations11
Published2017
Admission routes1
Has abstractyes

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