5715Anticoagulant use and associated outcomes in patients with atrial fibrillation and advanced kidney disease
Bibliographic record
Abstract
Background: Oral anticoagulants (OAC) reduce the risk of stroke but impose the risk of bleeding in patients with atrial fibrillation (AF). In the setting of advanced chronic kidney disease (CKD), the overall net benefit of OAC remains controversial. Purpose: Since patients with advanced CKD were excluded from recent pivotal trials of non-vitamin K antagonist oral anticoagulants (NOACs), we aimed to investigate the use and outcomes of warfarin and NOACs in patients with advanced CKD managed in contemporary routine clinical practice. Methods: Using a large U.S. administrative database, we identify 49,953 patients with AF and stage 4–5 CKD between 10/1/2010–1/31/2016. We used Cox proportional hazards models to assess the relationships between drug exposure and stroke or bleeding outcomes, adjusting for propensity scores calculated based on 59 socio-demographic and clinical characteristics. Results: Two-thirds of the patients were not treated with OAC, and a substantial minority (28%) were treated with warfarin. Outcomes were illustrated in the figure and remained unchanged if considering death as a competing risk or using other propensity score methods.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".