Predictors of Good Long-Term Renal Outcomes in Lupus Nephritis: Results from a Single Lupus Cohort
Bibliographic record
Abstract
This study aims to elucidate the predictive capabilities of proteinuria, serum creatinine (Cr), and urine RBCs (uRBCs) with respect to long-term renal outcomes in lupus nephritis (LN) in patients followed in clinic. Methods . A retrospective analysis was performed on patients with LN. We evaluated the ability of proteinuria, serum Cr, and uRBCs at 12 months to predict good long-term renal outcomes defined as serum Cr ≤ 100 mmol/L and kidney transplant/dialysis-free at the 7th year. Receiver operator characteristic curves were generated for proteinuria, serum Cr, and uRBCs to study their ability to predict good long-term outcomes and to identify their best cut-off. Descriptive statistics studied the pattern of change of proteinuria and serum Cr. Results . Proteinuria of 0.6 g/d and Cr of 83 mmol/L performed independently moderately well in predicting good long-term renal outcomes while uRBC was less accurate. Combining serum Cr to proteinuria gave a small increase in positive predictive value with a trade-off in sensitivity. Proteinuria changed within the first year whereas serum Cr changed until the 7th year. Conclusions . Both proteinuria and Cr predict good long-term renal outcomes in LN. Proteinuria’s ability to change faster at 12 months makes it a favorable endpoint for clinical trials and research studies.
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How this classification was reachedexpand
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".