Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients with chronic kidney disease (CKD) are at increased risk of atrial fibrillation, stroke, and bleeding posing unique clinical challenges. Novel oral anticoagulants (NOACs) including dabigatran, rivaroxaban, and apixaban have become recognized as alternative therapy to Vitamin K Antagonists (VKA) regarding the prevention of venous thromboembolism (VTE) and reduce the risk of stroke in atrial fibrillation. However, the understanding of NOACs in CKD is still underdeveloped. This review summarizes recent literature on the efficacy and safety of NOACs in patients with CKD. RECENT FINDINGS: Studies focusing on patients with moderate kidney disease were drawn from post hoc analyses from three major NOAC trials, meta-analyses, and postmarketing surveillance studies. Cumulatively, these studies continue to demonstrate NOACs as equivalent if not superior therapies to VKAs in regards to both efficacy and safety. These studies are limited by small sample sizes as well as a lack of direct comparison between NOACs. SUMMARY: The role of NOACs in managing VTE and atrial fibrillation is increasing. Current research suggests that NOACs are at least as efficacious and well tolerated as VKAs. More research is required to elucidate which NOAC is preferable in the clinical setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".