Atrial fibrillation and chronic kidney disease: struggling through thick and thin
Bibliographic record
Abstract
The prevalence of atrial fibrillation and the risk of stroke display an age-related increase in the chronic kidney disease (CKD) population. Evidence from large randomized controlled trials conducted in the general population supports the use of anticoagulation to reduce the risk of stroke in the setting of non-valvular atrial fibrillation. However, data in the non-dialysis-dependent and dialysis-dependent CKD populations are limited largely to observational studies, which demonstrate conflicting results regarding the risk-benefit profile of anticoagulation. The paradoxical increase in bleeding and thromboembolism that is observed in CKD further complicates decision-making on the use of anticoagulation. Several observational studies suggest an increased risk of bleeding that parallels the decline in renal function, with the highest rates of bleeding seen in the dialysis-dependent population, whereas other studies have not demonstrated any appreciable increase in bleeding risks with anticoagulation. Bleeding rates are largely driven by increased rates of gastrointestinal bleeding with anticoagulation, with minimal contribution of intra-cranial bleeding. Similarly, several studies have suggested lower rates of ischemic stroke and systemic thromboembolism with anticoagulation in people with CKD, whereas other studies have demonstrated no difference in rates of ischemic stroke. Given the paucity of high-quality evidence, and the high prevalence of atrial fibrillation in people with CKD, large randomized control trials are needed to provide recommendations for anticoagulation in this setting.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".