Update on the pathogenesis and treatment of childhood-onset systemic lupus erythematosus
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".