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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".