Interferon-α as a biomarker to predict renal outcomes in lupus nephritis
Bibliographic record
Abstract
OBJECTIVE: To determine if serum interferon (IFN)-α levels at the time of a lupus nephritis (LN) flare are associated with renal outcomes. METHODS: Patients with an LN flare who had a preflare estimated glomerular filtration rate (eGFR) ≥60 mL/min were included in the study. The following outcomes were ascertained: (1) Time to first and second LN flares during follow-up, (2) Time to a sustained decline in eGFR by 30% and 50%, and progression to end-stage renal disease (ESRD, <15 mL/min), and (3) Time to an adverse renal event (≥2 renal flares and/or at least a 30% sustained decline in eGFR during follow-up). Serum IFN-α was measured by Simoa. RESULTS: 92 patients with active LN were included in the study. Elevated serum baseline levels of IFN-α predicted poor renal outcomes. Patients with higher baseline IFN-α had a greater risk of having two or more subsequent LN flares (HR: 1.31 (1.08-1.59), p=0.006), sustained 30% decline in eGFR (HR: 1.27 (1.14-1.40), p<0.001), 50% decline in eGFR (HR: 1.27 (1.12-1.33), p<0.001) and progressing to ESRD (HR: 1.29 (1.14-1.47), p<0.001). Receiver operating characteristic analysis identified an IFN-α cut-off, 0.6 pg/ml, for predicting an adverse renal event. CONCLUSIONS: Elevated serum IFN-α levels measured at the time of an LN flare are associated with poor renal outcomes, including the development of ≥2 LN flares, and a clinically meaningful decline in kidney function.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".