Studying IgA Nephropathy at a Population Level over a 21-Year Period
Bibliographic record
Abstract
Background: Individuals with IgA nephropathy [IgAN] risk progression to end-stage kidney disease [ESKD] and death. Studies in IgAN to date have been mostly single- or multi-center research cohorts with inclusion criteria and selection bias that limits generalizability. Herein we report the methodology used to study outcomes of adult IgAN at the population level in a large Canadian province. Methods: This is a population-level cohort study of adults ≥18 years with IgAN using the British Columbia [BC] Glomerulonephritis [GN] Registry. All kidney biopsies are processed by nephropathologists in a single center. Any biopsy with GN is automatically registered in the BC GN Registry, which links with healthcare administrative databases to capture comorbidities, laboratory data, treatment and outcomes. Results: The BC GN Registry successfully captured 1382 individuals with primary IgAN over 21 years from 2000 to 2020. At the time of biopsy, median age was 44.3 years, eGFR was 54.1 ml/min/1.73m2, and proteinuria was 1.4 g/day (Table 1); 92.4% and 88.1% of patients had available eGFR and proteinuria. During follow-up, there were 19 (IQR 9, 38) and 12 (IQR 4, 25) eGFR and proteinuria measurements per patient, which were measured every 1.2 (IQR 0.4, 3.0) and 2.8 months (IQR 1.1, 4.8) respectively. The 20-year risk of ESKD was 49.4% and of death was 15.6% (Figure 1). Conclusion: We demonstrate the feasibility of using a provincial biopsy registry linked with administrative databases to study IgAN at the population-level in a large multi-ethnic Canadian province. The structure of the BC GN Registry captures all patients in BC with IgAN without selecting bias. Future steps will be to study treatment patterns, clinical outcomes, and healthcare utilization. Funding: Commercial Support - Novartis AGTable 1Figure 1
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| 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".