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Record W4416247836 · doi:10.3899/jrheum.2025-0504

The Renal Activity Index for Lupus: Validation for Prediction of Kidney Inflammation in Adult Patients With Lupus Nephritis

2025· article· en· W4416247836 on OpenAlexvenueno aff
Shannon K. O’Connor, Prasad Devarajan, Jinqi Liu, Michael A. Maldonado, Alyssa Sproles, James Rose, Sherry Thornton, Chen Chen, Hermine I. Brunner

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesGeorgia Clinical and Translational Science AllianceUniversity of CincinnatiNational Institutes of HealthCincinnati Children's Hospital Medical CenterPfizerBristol-Myers Squibb
KeywordsLupus nephritisInflammationKidneySystemic lupus erythematosusLupus erythematosusNephritisIndex (typography)Kidney disease

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the ability of the Renal Activity Index for Lupus (RAIL) score, a urine biomarker-derived score, to capture and predict the course of active lupus nephritis (LN) in adult patients. METHODS: Available serial urine samples collected up to week 52 from a subset of adults with active biopsy-proven proliferative LN participating in the double-blind randomized ALLURE trial of abatacept (ClinicalTrials.gov: NCT01714817) were used to calculate RAIL scores from creatinine-adjusted urine biomarkers (neutrophil gelatinase-associated lipocalin [NGAL], kidney injury molecule 1 [KIM-1], monocyte chemotactic protein 1 [MCP-1], adiponectin, hemopexin, ceruloplasmin). Discriminative performance of RAIL scores alone over time were compared with urine protein/creatinine ratio (UPCR), kidney function (estimated glomerular filtration rate [eGFR]), and mixed model analysis of RAIL score adjusted for baseline UPCR, eGFR, age, weight, sex, and race, with comparisons by renal response states including complete renal response (CRR), partial renal response but not CRR (PRR-only), and nonresponse (NR). RESULTS: The analysis included 240 patients who contributed 599 samples. At weeks 12/24/52, there were 44/22/15 patients with PRR-only, 27/33/18 with CRR, and 127/61/15 NR. RAIL scores, eGFR, and UPCR improved over time irrespective of abatacept use, but were significantly lower with CRR compared to NR. The eGFR alone had poor accuracy (area under the receiver-operating characteristic curve [AUC] < 0.51) to discriminate renal response. Only after correction of baseline UPCR and eGFR, the RAIL score had excellent accuracy to reflect CRR from other renal response states at the current (AUC = 0.83-0.84) and next visit (AUC = 0.84-0.85) and performed better than UPCR; without correction, UPCR and RAIL score had similarly good accuracy. CONCLUSION: RAIL scores identify active LN and longitudinally predict the course of adult LN. (ClinicalTrials.gov: NCT01714817).

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.011
GPT teacher head0.273
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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