Cardiovascular risk is similar in patients with glomerulonephritis compared to other types of chronic kidney disease: a matched cohort study
Bibliographic record
Abstract
BACKGROUND: Patients with chronic kidney disease (CKD) due to glomerulonephritis (GN) are thought to be at high risk for cardiovascular disease (CVD). However, no study has examined whether GN directly contributes to CV risk beyond the effects conferred by pre-existing traditional risk factors and level of renal function. METHODS: ) from 25 Canadian centres were divided into GN vs non-GN cause of CKD. Patients on immunotherapy for GN were not included in the study. Standardized measures of CV risk factors, biomarkers and CV outcomes were recorded over 3 years of follow-up, with the primary outcome measure being time to first all-cause CV event. RESULTS: In the overall cohort, CV events occurred in 25 (8.7%) of the GN group and 338 (17.8%) of the non-GN group (HR 0.45, 95% CI 0.30-0.67, p < 0.01). In a Cox regression multivariable model that included age, sex, prior diabetes and CVD, baseline eGFR and onset of renal replacement therapy, the risk of CV events was similar in the GN and non-GN groups (HR 0.71, 95% CI 0.47-1.08, p = 0.11). GN and non-GN patients were matched by age and using a propensity score including sex, prior diabetes and CVD and baseline eGFR. In the matched group, the risk of CV events was similar in GN vs non-GN patients (N = 25/271 (9.2%) in both groups, HR 1.01, 95% CI 0.05-1.77, p = 0.9). An interaction analysis showed that CRP, ACR and troponin conferred differing amounts of CV risk in the GN and non-GN groups. CONCLUSIONS: Patients with advanced CKD due to GN have a high 8.7% absolute 3-year risk of CVD, attributable to prior CV risk factors and level of kidney function rather than the GN disease itself.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".