Interferon-α as a biomarker to predict renal outcomes in lupus nephritis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it