Comparison of novel oral anticoagulants versus vitamin K antagonists in patients with chronic kidney disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Novel oral anticoagulants (NOACs) including apixaban, dabigatran and rivaroxaban have been approved by international regulatory agencies to prevent venous thromboembolism as well as treat atrial fibrillation and venous thromboembolism in individuals with chronic kidney disease (CKD). However, alterations in their metabolism in the setting of CKD may impact their efficacy and lead to an increased risk of bleeding. This review summarizes the current literature on the efficacy and safety of these agents in individuals with moderate CKD. RECENT FINDINGS: In clinical trials, the use of the NOACs in patients with moderate CKD has demonstrated efficacy and safety similar to those seen with vitamin K antagonists. However, no universal reversal agent for the anticoagulant effect of the NOACs exists in the setting of bleeding. Limited data have demonstrated that hemodialysis has been effectively used to aid in reversing the effects of dabigatran, and the use of prothrombin complex concentrate has also been used for serious and major adverse bleeding events with some success. SUMMARY: As the use of the NOACs in patients with CKD increases, it will be important to monitor their safety, and clinicians who prescribe them should carefully monitor kidney function and recognize the potential for adverse effects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".