Kidney Biopsy in Management of Lupus Nephritis: A Case-Based Narrative Review
Bibliographic record
Abstract
Kidney involvement in patients with lupus highly increases morbidity and mortality. In recent years, several reports have emphasized the dissociation between clinical and histological findings and highlighted the role of kidney biopsy as an instrument for diagnosis and follow-up of lupus nephritis. The kidney biopsy at initial diagnosis allows an early diagnosis, assessment of activity and chronicity, and detection of nonimmune complex nephritis. A kidney biopsy repeated months after treatment aids in the detection of persistent histological inflammation, which has been linked to the occurrence of future kidney relapses. A kidney biopsy at a relapse detects histological changes including chronic scarring. Finally, a kidney biopsy in patients with a clinical response undergoing maintenance immunosuppression may aid therapy tapering and/or suspension. The evidence supporting the use of a kidney biopsy in different scenarios across the course of lupus nephritis is heterogeneous, with most reports assessing the value for the diagnosis of a first or relapsing flare. In contrast, less evidence suggests additional therapeutic-modifying information derived from repeat posttreatment biopsies and biopsies to evaluate treatment tapering or suspension. In this clinical case-based review, we examine the role of kidney biopsy as a tool to improve clinical outcomes of patients with lupus nephritis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".