Venous Thromboembolism in Patients with Membranous Nephropathy
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it