A Longitudinal Analysis of Outcomes of Lupus Nephritis in an International Inception Cohort Using a Multistate Model Approach
Bibliographic record
Abstract
OBJECTIVE: To study bidirectional change and predictors of change in estimated glomerular filtration rate (GFR) and proteinuria in lupus nephritis (LN) using a multistate modeling approach. METHODS: Patients in the Systemic Lupus International Collaborating Clinics inception cohort were classified annually into estimated GFR state 1 (>60 ml/minute), state 2 (30-60 ml/minute), or state 3 (<30 ml/minute) and estimated proteinuria state 1 (<0.25 gm/day), state 2 (0.25-3.0 gm/day), or state 3 (>3.0 gm/day), or end-stage renal disease (ESRD) or death. Using multistate modeling, relative transition rates between states indicated improvement and deterioration. RESULTS: Of 1,826 lupus patients, 700 (38.3%) developed LN. During a mean ± SD follow-up of 5.2 ± 3.5 years, the likelihood of improvement in estimated GFR and estimated proteinuria was greater than the likelihood of deterioration. After 5 years, 62% of patients initially in estimated GFR state 3 and 11% of patients initially in estimated proteinuria state 3 transitioned to ESRD. The probability of remaining in the initial states 1, 2, and 3 was 85%, 11%, and 3%, respectively, for estimated GFR and 62%, 29%, and 4%, respectively, for estimated proteinuria. Male sex predicted improvement in estimated GFR states; older age, race/ethnicity, higher estimated proteinuria state, and higher renal biopsy chronicity scores predicted deterioration. For estimated proteinuria, race/ethnicity, earlier calendar years, damage scores without renal variables, and higher renal biopsy chronicity scores predicted deterioration; male sex, presence of lupus anticoagulant, class V nephritis, and mycophenolic acid use predicted less improvement. CONCLUSION: In LN, the expected improvement or deterioration in renal outcomes can be estimated by multistate modeling and is preceded by identifiable risk factors. New therapeutic interventions for LN should meet or exceed these expectations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".