Anticoagulant-Related Nephropathy
Bibliographic record
Abstract
Anticoagulant-related nephropathy (ARN) is a newly recognized form of AKI in which overanticoagulation causes profuse glomerular hemorrhage, which manifests on renal biopsy as numerous renal tubules filled with red cells and red cell casts. The glomeruli show changes, but they are not sufficient to account for the glomerular hemorrhage. We were the first to study ARN, and since then, our work has been confirmed by numerous other investigators. Oral anticoagulants have been in widespread use since the 1950s; today, >2 million patients with atrial fibrillation take an oral anticoagulant. Despite this history of widespread and prolonged exposure to oral anticoagulants, ARN was discovered only recently, suggesting that the condition may be a rare occurrence. This review chronicles the discovery of ARN, its confirmation by others, and our animal model of ARN. We also provide new data on analysis of “renal events” described in the post hoc analyses of three pivotal anticoagulation trials and three retrospective analyses of large clinical databases. Taken together, these analyses suggest that ARN is not a rare occurrence in the anticoagulated patient with atrial fibrillation. However, much work needs to be done to understand the condition, particularly prospective studies, to avoid the biases inherent in post hoc and retrospective analyses. Finally, we provide recommendations regarding the diagnosis and management of ARN on the basis of the best information available.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".