Risk of major bleeding in recurrent fallers receiving anticoagulation for atrial fibrillation: a prospective cohort study
Bibliographic record
Abstract
Background: The risk of fall-related bleeding is a frequent reason for not following recommendations on anticoagulation in patients with atrial fibrillation (AF). Objectives: To assess whether patients on anticoagulation therapy with recurrent falls are at an increased risk of bleeding. Methods: We used data from the Swiss-AF multicenter cohort study, including patients aged ≥65 years with documented AF and oral anticoagulant treatment. Recurrent fallers were defined as those reporting > 1 fall/y. The primary outcome was first major bleeding as defined by the International Society on Thrombosis and Haemostasis. To account for death as a competing event, we used the Fine-Gray competing risk regression model to examine the association between a history of recurrent falls and time to a first major bleeding event. The results were expressed as subdistribution hazard ratios with 95% CIs. Results: We included 2154 patients (mean age, 73.4 years; 27.5% female), 180 (8.3%) of whom reported recurrent falls. During a median follow-up of 36 months, 368 (17.1%) patients died, and 218 (10.1%) had a first major bleeding event. Recurrent fallers were more likely to experience trauma-related bleeding episodes than nonfallers (16.7% vs 9.2%). The adjusted subdistribution hazard ratio for major bleeding in recurrent fallers was 1.16 (95% CI, 0.74-1.82). In subgroup analyses of patients receiving direct oral anticoagulants or vitamin K antagonists, the risk of major bleeding was not increased for recurrent fallers. Conclusion: We found no association between recurrent falls and risk of major bleeding in AF patients receiving direct oral anticoagulants or vitamin K antagonists.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".