Histopathologic Features Aid in Predicting Risk for Progression of IgA Nephropathy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: IgA nephropathy (IgAN) is the most common primary glomerular disease worldwide. Accurately identifying patients who are at risk for progressive disease is challenging. The extent to which histopathologic features improves prognostication is uncertain. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We studied a retrospective cohort with biopsy-proven IgAN in Calgary, Canada. Renal biopsies were reviewed by a nephropathologist with histopathologic data abstracted using a standardized form. The primary outcome was the composite of doubling of serum creatinine, ESRD, or death. Spline models defined significant levels of interstitial fibrosis, glomerulosclerosis, hypertension, proteinuria, and creatinine. The prognostic significances of clinical and histopathologic parameters were determined using Cox proportional hazards models. RESULTS: Data from 146 cases were available for analysis with a median follow-up of 5.8 years. Greater than 25% interstitial fibrosis, >40% glomerular sclerosis, and a systolic BP >150 mmHg were risk thresholds. In univariable analyses, baseline creatinine, proteinuria, systolic BP, glomerular sclerosis, interstitial fibrosis, and crescentic disease were predictors of the primary outcome. In multivariable models adjusted for clinical characteristics, interstitial fibrosis (hazard ratio [HR]2.7; 95% confidence interval [CI] 1.2 to 6.0), glomerular sclerosis (HR 2.6; 95% CI 1.2 to 4.5), and crescents (HR 2.4; 95% CI 1.2 to 5.1) remained independent predictors of the primary outcome and significantly improved model fit compared with clinical characteristics alone. CONCLUSIONS: Baseline histopathologic parameters are independent predictors of adverse outcomes in IgAN even after taking into consideration clinical characteristics. Relatively small degrees of interstitial fibrosis confer an increased risk for progressive IgAN.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".