Development of a Novel Renal Activity Index of Lupus Nephritis in Children and Young Adults
Bibliographic record
Abstract
OBJECTIVE: Noninvasive estimation of the degree of inflammation seen on kidney biopsy with lupus nephritis (LN) remains difficult. The objective of this study was to develop a Renal Activity Index for Lupus (RAIL) that, based solely on laboratory measures, accurately reflects histologic LN activity. METHODS: We assayed traditional LN laboratory tests and 16 urine biomarkers (UBMs) in children (n = 47) at the time of kidney biopsy. Histologic LN activity was measured by the National Institutes of Health activity index (NIH-AI) and the tubulointerstitial activity index (TIAI). High LN-activity status (versus moderate/low) was defined as NIH-AI scores >10 (versus ≤10) or TIAI scores >5 (versus ≤5). RAIL algorithms that predicted LN-activity status for both NIH-AI and TIAI were derived by stepwise multivariate logistic regression, considering traditional biomarkers and UBMs as candidate components. The accuracy of the RAIL for discriminating by LN-activity status was determined. RESULTS: The differential excretion of 6 UBMs (neutrophil gelatinase-associated lipocalin, monocyte chemotactic protein 1, ceruloplasmin, adiponectin, hemopexin, and kidney injury molecule 1) standardized by urine creatinine was considered in the RAIL. These UBMs predicted LN-activity (NIH-AI) status with >92% accuracy and LN-activity (TIAI) status with >80% accuracy. RAIL accuracy was minimally influenced by concomitant LN damage. Accuracies between 71% and 85% were achieved without standardization of the UBMs. The strength of these UBMs to reflect LN-activity status was confirmed by principal component and linear discriminant analyses. CONCLUSION: The RAIL is a robust and highly accurate noninvasive measure of LN activity. The measurement properties of the RAIL, which reflect the degree of inflammatory changes as seen on kidney biopsy, will require independent validation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".