Effects of Dabigatran versus Traditional Warfarin on Major Bleeding Events in Atrial Fibrillation Patients: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background: This systematic review and meta-analysis investigate the effects of dabigatran compared to traditional warfarin on major bleeding events in patients with atrial fibrillation. By synthesising data from relevant studies, this analysis aims to provide insights into the comparative safety profiles of these anticoagulant medications in managing atrial fibrillation. Materials and Methods: The search strategy focused on electronic databases using keywords related to dabigatran, warfarin and atrial fibrillation, with a language restriction to English. Quality assessment was conducted using the Newcastle–Ottawa Scale to evaluate bias in the selected studies. The study selection criteria included retrospective trials comparing bleeding complications between dabigatran and warfarin in atrial fibrillation patients. Data extraction and quality assessment were conducted independently by two investigators. Statistical analyses were performed using R software to assess heterogeneity and risk ratios, employing both random-effects and common-effect models for comprehensive insights. Results: The meta-analysis revealed a significant association between dabigatran and major bleeding events compared to warfarin, yet with notable heterogeneity across studies. While no significant publication bias was evident, caution was warranted due to uncertainty in certain estimates. The variability in study quality underscored the need for meticulous methodological appraisal. Conclusions: These findings enhance our understanding of anticoagulant safety profiles, guiding clinical practice and future research in the management of atrial fibrillation.
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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".