Bibliographic record
Abstract
BACKGROUND: Lupus nephritis (LN) is one of the most common severe organ manifestations of systemic lupus erythematosus (SLE). LN is associated with significant morbidity and mortality in SLE patients, as up to 20% of patients progress to end-stage renal disease (ESRD). The clinical manifestations of LN are variable, ranging from asymptomatic proteinuria to a myriad of manifestations associated with nephritic and nephrotic syndromes and ESRD. It is therefore important to screen all SLE patients for LN. CONTENT: Urinalysis is a useful screening test in LN. Quantification of proteinuria can be performed with either a urine protein-to-creatinine ratio or 24-h urine sample collection for protein. Renal biopsy remains the gold standard for diagnosis of LN. Traditional serum biomarkers used to monitor SLE and LN disease activity and flares include anti-double-stranded DNA antibodies and complement components 3 and 4. Other nonconventional biomarkers found to correlate with LN include anti-C1q and surrogate markers of type 1 interferon regulatory genes (INF gene signature). Potential urinary biomarkers for LN include monocyte chemoattractant protein 1, neutrophil gelatinase-associated lipocalin, tumor necrosis factor-like inducer of apoptosis, and vascular cell adhesion molecule 1. SUMMARY: Although studies have shown promising results for the use of alternative biomarkers, these require validation in prospective studies to support their use. Renal remission rates in patients receiving standard of care therapy for induction and maintenance treatment of LN remain low. This has prompted further research in newer therapeutic targets in LN ,which have shown promising results.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".