Is there long-term value of pathology scoring in immunoglobulin A nephropathy? A validation study of the Oxford Classification for IgA Nephropathy (VALIGA) update
Bibliographic record
Abstract
BACKGROUND: It is unknown whether renal pathology lesions in immunoglobulin A nephropathy (IgAN) correlate with renal outcomes over decades of follow-up. METHODS: In 1130 patients of the original Validation Study of the Oxford Classification for IgA Nephropathy (VALIGA) cohort, we studied the relationship between the MEST score (mesangial hypercellularity, M; endocapillary hypercellularity, E; segmental glomerulosclerosis, S; tubular atrophy/interstitial fibrosis, T), crescents (C) and other histological lesions with both a combined renal endpoint [50% estimated glomerular filtration rate (eGFR) loss or kidney failure] and the rate of eGFR decline over a follow-up period extending to 35 years [median 7 years (interquartile range 4.1-10.8)]. RESULTS: In this extended analysis, M1, S1 and T1-T2 lesions as well as the whole MEST score were independently related with the combined endpoint (P < 0.01), and there was no effect modification by age for these associations, suggesting that they may be valid in children and in adults as well. Only T lesions were associated with the rate of eGFR loss in the whole cohort, whereas C showed this association only in patients not treated with immunosuppression. In separate prognostic analyses, the whole set of pathology lesions provided a gain in discrimination power over the clinical variables alone, which was similar at 5 years (+2.0%) and for the whole follow-up (+1.8%). A similar benefit was observed for risk reclassification analyses (+2.7% and +2.4%). CONCLUSION: Long-term follow-up analyses of the VALIGA cohort showed that the independent relationship between kidney biopsy findings and the risk of progression towards kidney failure in IgAN remains unchanged across all age groups and decades after the renal biopsy.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".