A Multicenter Study of the Predictive Value of Crescents in IgA Nephropathy
Bibliographic record
Abstract
The Oxford Classification of IgA nephropathy does not account for glomerular crescents. However, studies that reported no independent predictive role of crescents on renal outcomes excluded individuals with severe renal insufficiency. In a large IgA nephropathy cohort pooled from four retrospective studies, we addressed crescents as a predictor of renal outcomes and determined whether the fraction of crescent-containing glomeruli associates with survival from either a ≥50% decline in eGFR or ESRD (combined event) adjusting for covariates used in the original Oxford study. The 3096 subjects studied had an initial mean±SD eGFR of 78±29 ml/min per 1.73 m 2 and median (interquartile range) proteinuria of 1.2 (0.7–2.3) g/d, and 36% of subjects had cellular or fibrocellular crescents. Overall, crescents predicted a higher risk of a combined event, although this remained significant only in patients not receiving immunosuppression. Having crescents in at least one sixth or one fourth of glomeruli associated with a hazard ratio (95% confidence interval) for a combined event of 1.63 (1.10 to 2.43) or 2.29 (1.35 to 3.91), respectively, in all individuals. Furthermore, having crescents in at least one fourth of glomeruli independently associated with a combined event in patients receiving and not receiving immunosuppression. We propose adding the following crescent scores to the Oxford Classification: C0 (no crescents); C1 (crescents in less than one fourth of glomeruli), identifying patients at increased risk of poor outcome without immunosuppression; and C2 (crescents in over one fourth of glomeruli), identifying patients at even greater risk of progression, even with immunosuppression.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".