Characterizing the NIH Activity and Chronicity Indices in 2 Independent Lupus Nephritis Cohorts
Bibliographic record
Abstract
Introduction The inclusion of National Institutes of Health (NIH) activity (AI) and chronicity (CI) indices in the International Society of Nephrology/Renal Pathology Society (ISN/RPS) classification of lupus nephritis (LN) aims to provide a precise characterization of the amount of active and chronic lesions next to lupus class. We here investigate the distribution of NIH indices within two international LN cohorts, their relationship with the ISN/RPS classes and which lesions most significantly contribute to these scores. Methods We collected 194 biopsies from two cohorts of patients with LN and calculated the NIH AI and CI according to the revised 2018 ISN/RPS classification. For statistical analysis we mainly used non-parametric tests. An exploratory factor analysis was applied to the lesion scores. Results The NIH AI score was usually medium-low, reaching a maximum value of 16/24, while the NIH CI reached 10/12. Both indices were higher in classes III, IV and mixed compared to others (p<.0001). Endocapillary hypercellularity was present in more than 70% biopsies, showing a strong correlation with neutrophils/karyorrhexis (r=0.78, p<.0001) and cellular crescents (p<.0001). Chronic lesions showed a strong correlation with each other (p<.0001), except for fibrous crescents which had the strongest correlation with cellular crescents (r=0.33, p<.0001). The inclusion of all lesions in an exploratory factor analysis uncovered two underlying main factors that accurately reflect the NIH AI and CI. Conclusion This study revealed key aspects of the NIH AI and CI that may guide future modifications of these indices, leading to a more balanced and reliable scoring system.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".