Venous Thromboembolism in Patients with Membranous Nephropathy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The aims of this study were to determine the frequency of venous thromboembolic events in a large cohort of patients with idiopathic membranous nephropathy and to identify predisposing risk factors. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We studied patients with biopsy-proven membranous nephropathy from the Glomerular Disease Collaborative Network (n=412) and the Toronto Glomerulonephritis Registry (n=486) inception cohorts. The cohorts were pooled after establishing similar baseline characteristics (total n=898). Clinically apparent and radiologically confirmed venous thromboembolic events were identified. Potential risk factors were evaluated using multivariable logistic regression models. RESULTS: Sixty-five (7.2%) subjects had at least one venous thromboembolic event, and this rate did not differ significantly between registries. Most venous thromboembolic events occurred within 2 years of first clinical assessment (median time to VTE = 3.8 months). After adjusting for age, sex, proteinuria, and immunosuppressive therapy, hypoalbuminemia at diagnosis was the only independent predictor of a venous thromboembolic event. Each 1.0 g/dl reduction in serum albumin was associated with a 2.13-fold increased risk of VTE. An albumin level <2.8 g/dl was the threshold below which risk for a venous thromboembolic event was greatest. CONCLUSIONS: We conclude that clinically apparent venous thromboembolic events occur in about 7% of patients with membranous nephropathy. Hypoalbuminemia, particularly <2.8 g/dl, is the most significant independent predictor of venous thrombotic risk.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".