Responsive and monophasic lupus nephritis: prevalence, associations and outcomes
Bibliographic record
Abstract
OBJECTIVES: LN is associated with significant renal morbidity, although some patients may experience a responsive and monophasic course. We aimed to assess the proportion of LN patients with such a course and identify its associated baseline characteristics. METHODS: This retrospective study included SLE patients from an observational cohort who developed LN at or after clinic entry. A responsive and monophasic course was defined as complete proteinuria recovery within 1 year for subnephrotic- or 2 years for nephrotic-range proteinuria, without subsequent renal flares. Univariable and multivariable logistic regression were used to assess the impact of baseline characteristics on developing a responsive and monophasic course, while Cox regression was used to explore the association with a long-term adverse composite outcome, including end-stage kidney disease, sustained ≥30% estimated glomerular filtration rate (eGFR) decline, or death. RESULTS: Among 374 LN patients (mean age: 32.8 years; 89.2% female; median follow-up: 10.6 years), 96 patients (25.7%) exhibited a responsive and monophasic course, which was associated with older age [odds ratio (OR) 1.04, 95% CI 1.01-1.07] and shorter SLE duration (OR 0.91, 95% CI 0.86-0.97) at LN onset. In the Cox model, timely recovery of proteinuria and the absence of subsequent LN flares were associated with a reduced risk of the adverse composite outcome. Despite these associations, 18.8% of patients with a responsive and monophasic course still experienced adverse events. CONCLUSION: Approximately 26% of LN patients exhibit a responsive and monophasic course. Predicting such a course remains challenging; despite improved outcomes, 18.8% of these patients still experience adverse long-term events.
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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 | 0.002 |
| 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 | 0.001 |
| 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".