Repeat Kidney Biopsies and Class Switching in Pediatric Lupus Nephritis
Bibliographic record
Abstract
Background: Lupus nephritis (LN) remains a common complication associated with increased morbidity and mortality in patients with systemic lupus erythematosus (SLE). Patients with LN often experience flares that can compromise renal function and complicate disease management. Repeat kidney biopsies, often performed during flares or for surveillance, may reveal a change in the pathologic class of LN, a phenomenon known as class switching. While class switching has been well-described in adults, data for pediatric lupus nephritis (pLN) is limited. Methods: The Prospective Pediatric Lupus Nephritis Registry (ProPeL) is a multicenter, prospective study from the Pediatric Nephrology Research Consortium (PNRC) that enrolled pLN patients <21 years of age within 4 weeks of an initial kidney biopsy diagnostic of pLN, with follow-up for up to 5 years. Among 112 patients with biopsy-proven Class III, IV, V or mixed LN, a cohort of 39 underwent repeat kidney biopsies. We evaluated this cohort regarding the frequency of class switching and degree of interstitial fibrosis progression. Results: On repeat kidney biopsy, class severity decreased overall—Class I, II, and V became more common compared to initial Class IV/IV+V (Figure 1). However, interstitial fibrosis worsened in degree. Class switching occurred in most patients: 56.4% improved, 15.4% worsened, and 28.2% remained unchanged. These findings suggest that histologic evolution is common in pediatric LN, regardless of treatment status. Conclusion: This is the first prospective pLN study showing class switching is common. Despite standard therapy, nearly 15% of patients showed progression to worsening LN class or degree of fibrosis, highlighting current treatment limitations. These findings support the need for better monitoring tools and standardized indications for repeat biopsies to improve long-term outcomes.Figure 1: Initial vs Repeat Biopsy Class
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".