Risk of Renal Failure Within 10 or 20 Years of Systemic Lupus Erythematosus Diagnosis
Bibliographic record
Abstract
Objective. The frequency of endstage renal disease (ESRD) from systemic lupus erythematosus (SLE) in the United States has not improved over the last few decades in large population datasets. Understanding the risk factors for renal failure in SLE could lead to earlier detection of lupus nephritis and potentially more effective treatments in those with markers of poor prognosis. Methods. The Hopkins Lupus Cohort, comprising 2528 patients was used. One hundred fifty-one patients experienced renal failure after SLE diagnosis, defined as dialysis or renal transplant. We estimated the risk of renal failure in subgroups defined by demographics, laboratory tests, and the American College of Rheumatology/Systemic Lupus International Collaborating Clinics (ACR/SLICC) classification criteria satisfied within 1 year of SLE diagnosis. Results. The overall incidence of renal failure within 20 years of SLE diagnosis was 8.4%. The risk was much higher (20.0%) among those who experienced proteinuria within the first year of diagnosis. Demographic predictors included African American ethnicity [rate ratio (RR) 1.82, P = 0.0012] and age ≥ 40 years at SLE diagnosis (RR 0.51 vs those with diagnosis at < 30 yrs of age, P = 0.019). Among immunologic markers, low C3 was a strong predictor of renal failure (RR 2.00, P = 0.0011). Conclusion. Proteinuria within the first year of diagnosis of SLE is one of the most important predictors of ESRD. Our data also confirm African American ethnicity, younger age at SLE diagnosis, and low C3 as strong predictors of renal failure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".