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Record W2465861967 · doi:10.1097/bor.0000000000000317

Update on the pathogenesis and treatment of childhood-onset systemic lupus erythematosus

2016· review· en· W2465861967 on OpenAlexaff
Julie Couture, Earl D. Silverman

Bibliographic record

VenueCurrent Opinion in Rheumatology · 2016
Typereview
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsSickKids FoundationHospital for Sick ChildrenMcGill UniversityUniversity of TorontoMontreal Children's Hospital
Fundersnot available
KeywordsMedicineDiseaseEpidemiologySystemic lupus erythematosusLupus nephritisIntensive care medicineLupus erythematosusClinical trialBiomarkerPediatricsImmunologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This article will provide an update of studies published in the last year regarding epidemiology, pathogenesis, major disease manifestations and outcomes, and therapies in childhood-onset systemic lupus erythematosus (cSLE). RECENT FINDINGS: Recent studies on cSLE epidemiology supported previous findings that cSLE patients have more severe disease and tend to accumulate damage rapidly. Lupus nephritis remains frequent and is still a significant cause of morbidity and mortality. In the past year unfortunately there were no new reproducible, biomarker studies to help direct therapy of renal disease. However, some progress was made in neuropsychiatric disease assessment, with a new and promising automated test to screen for cognitive dysfunction reported. There were no prospective interventional treatment trials designed for patients with cSLE published in the last year, but some studies involving children are currently active and might improve the therapeutic options for patients with cSLE. SUMMARY: There is a need to get a better understanding of pathogenesis and identify new biomarkers in cSLE to more accurately predict outcomes. New insights into characterization of different clinical manifestations may enable to optimize individual interventions and influence the prognosis.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.685
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
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 head0.084
GPT teacher head0.378
Teacher spread0.294 · 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 designOther design
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

Citations47
Published2016
Admission routes1
Has abstractyes

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