Warfarin adherence and anticoagulation control in atrial fibrillation patients: a systematic review.
Bibliographic record
Abstract
OBJECTIVE: This paper aims to review the literature systematically on warfarin adherence in patients with atrial fibrillation and to assess the anticoagulation control focused on patient clinical outcomes. Atrial Fibrillation (AF) is a cardiac disease defined by abnormal heart rhythm, which significantly impacts a patient's health status, quality of life, and heart output, and thus a greater risk of stroke and hospitalization. Most AF patients should be managed with long-term anticoagulation, either with vitamin K antagonists such as warfarin or new oral anticoagulants (NOACs). Anticoagulants have been a core in treating AF and weighing the consequences of thrombosis with the risk of bleeding. This systematic review aimed to assess the impact of warfarin adherence on AF patients. MATERIALS AND METHODS: A systematic search of the literature was conducted on electronic databases of PubMed/MEDLINE, EBSCO, Cochrane library, Google, and Google Scholar from January 2011 to April 2021 to determine studies that reported warfarin adherence on patients with atrial fibrillation. RESULTS: Out of 1429 titles and abstracts were retrieved, 12 studies fulfilled and met the inclusion criteria. From the included studies, two were carried out in Brazil and one from the following nations: Libya, Jordan, Iran, KSA, Canada, Malaysia, Bahrain, UAE, Singapore, and the USA. The study designs identified were cross-section, retrospective, and prospective studies. Warfarin adherence was influenced by multiple causes, including pharmaceutical services, the number of medications, and warfarin knowledge regarding anticoagulation control. Warfarin adherence illustrates its positive association with TTR and INR as a measure of anticoagulation control. CONCLUSIONS: While the available evidence is limited, this systematic review demonstrated a positive finding of the association between warfarin adherence and anticoagulation control in patients with AF.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".