Lupus nephritis trials network (LNTN) repeat kidney biopsy-based definitions of treatment response: A systematic literature review-based proposal
Bibliographic record
Abstract
Within the frame of the Lupus Nephritis Trials Network (LNTN), we conducted a systematic literature review (SLR) to propose kidney tissue-based definitions of treatment outcomes in lupus nephritis (LN). Given the limitations of clinical markers like proteinuria in predicting immunological, histological, and long-term outcomes, our work emphasises the importance of repeat kidney biopsies. Such biopsies help identify discordance between clinical and histological response, which has implications for long-term kidney outcomes. The research objectives of this SLR focused on defining repeat biopsy-based treatment response and histological remission, and their associations with long-term outcomes. The SLR reviewed studies published from 2000 to 2022, identifying 20 eligible works. Histological response was commonly defined by changes in the National Institutes of Health (NIH) Activity Index (AI), with response indicated by a decrease of ≥50 % and to ≤3. Remission was most commonly defined as an AI score of 0. These benchmarks were associated with improved long-term renal outcomes, such as reduced flare rates and preserved kidney function. Conversely, NIH AI scores ≥4 and NIH Chronicity Index (CI) scores ≥4 were associated with poor prognosis, highlighting their predictive utility. Consensus definitions were established through expert panel deliberation, setting a foundation for standardising LN treatment evaluation in clinical trials and observational studies. These definitions are not intended for routine clinical decisions but aim to enhance uniformity and comparability in research, especially when repeat kidney biopsies are performed, an approach strongly advocated by our work. Further validation through ongoing initiatives and molecular characterisation efforts will refine these criteria, fostering advances in LN management and patient outcomes.
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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 | 0.141 | 0.249 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.039 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".