The Canadian Glomerulonephritis Registry (CGNR) and Translational Research Initiative: Rationale and Clinical Research Protocol
Bibliographic record
Abstract
Background: Glomerulonephritis (GN) is a leading cause of kidney failure and accounts for 20% of incident cases of end-stage kidney disease (ESKD) in Canada annually. Reversal of kidney injury and prevention of progression to kidney failure is possible; however, limited knowledge of underlying disease mechanisms and lack of noninvasive biomarkers and therapeutic targets are major barriers to successful therapeutic intervention. Multicenter approaches that link longitudinal clinical and outcomes data with serial biologic specimen collection would help bridge this gap. Objective: To establish a national, patient-centered, multidimensional web-based clinical database and federated virtual biobank to conduct human-based molecular and clinical research in GN in Canada. Design: Multicenter, prospective observational registry, starting in 2019. Setting: Nine participating Canadian tertiary care centers. Patients: Adult patients with a histopathologic pattern of injury consistent with IgA nephropathy, focal and segmental glomerulosclerosis, minimal change disease, membranous nephropathy, C3 glomerulopathy, and membranoproliferative GN recruited within 24 months of biopsy. Measurements: Initial visits include detailed clinical, histopathological, and laboratory data collection, blood, urine, and tonsil swab biospecimen collection, and a self-administered quality of life questionnaire. Follow-up clinical and laboratory data collection, biospecimen collection, and questionnaires are obtained every 6 months thereafter. Methods: Patients receive care as defined by their physician, with study visits scheduled every 6 months. Patients are followed until death, dialysis, transplantation, or withdrawal from the study. Key outcomes include a composite of ESKD or a 40% decline in estimated glomerular filtration rate (eGFR) at 2 years, rate of kidney function decline, and remission of proteinuria. Clinical and molecular phenotypical data will be analyzed by GN subtype to identify disease predictors and discover therapeutic targets. Limitations: Given the relative rarity of individual glomerular diseases, one of the major challenges is patient recruitment. Initial registry studies may be underpowered to detect small differences in clinically meaningful outcomes such as ESKD or death due to small sample sizes and short duration of follow-up in the initial 2-year phase of the study. Conclusions: The Canadian Glomerulonephritis Registry (CGNR) supports national collaborative efforts to study glomerular disease patients and their outcomes. Trial registration: NCT03460054.
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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 | 0.047 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".