Validation of the Lupus Nephritis Clinical Indices in Childhood‐Onset Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To validate clinical indices of lupus nephritis activity and damage when used in children against the criterion standard of kidney biopsy findings. METHODS: In 83 children requiring kidney biopsy, the Systemic Lupus Erythematosus Disease Activity Index renal domain (SLEDAI-R), British Isles Lupus Assessment Group index renal domain (BILAG-R), Systemic Lupus International Collaborating Clinics (SLICC) renal activity score (SLICC-RAS), and SLICC Damage Index renal domain (SDI-R) were measured. Fixed effects and logistic models were calculated to predict International Society of Nephrology/Renal Pathology Society (ISN/RPS) class; low-to-moderate versus high lupus nephritis activity (National Institutes of Health [NIH] activity index [AI]) score: ≤10 versus >10; tubulointerstitial activity index (TIAI) score: ≤5 versus >5; or the absence versus presence of lupus nephritis chronicity (NIH chronicity index) score: 0 versus ≥1. RESULTS: There were 10, 50, and 23 patients with ISN/RPS class I/II, III/IV, and V, respectively. Scores of the clinical indices did not differentiate among patients by ISN/RPS class. The SLEDAI-R and SLICC-RAS but not the BILAG-R differed with lupus nephritis activity status defined by NIH-AI scores, while only the SLEDAI-R scores differed between lupus nephritis activity status based on TIAI scores. The sensitivity and specificity of the SDI-R to capture lupus nephritis chronicity was 23.5% and 91.7%, respectively. Despite being designed to measure lupus nephritis activity, SLICC-RAS and SLEDAI-R scores significantly differed with lupus nephritis chronicity status. CONCLUSION: Current clinical indices of lupus nephritis fail to discriminate ISN/RPS class in children. Despite its shortcomings, the SLEDAI-R appears best for measuring lupus nephritis activity in a clinical setting. The SDI-R is a poor correlate of lupus nephritis chronicity.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".