Direct Oral Anticoagulants vs Vitamin K Antagonists in Left Ventricular Thrombi: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is increasing interest in direct oral anticoagulants (DOACs), given their safety and convenience in atrial fibrillation, compared with vitamin K antagonists (VKAs). However, the use of DOACs in left ventricular (LV) thrombi is considered off-label, with current guidelines recommending VKAs. The aim of this meta-analysis was to compare the safety and efficacy of DOACs to VKAs in the management of LV thrombi. METHODS: A systematic search was conducted for studies published between January 1, 2009 and January 31, 2021 in PubMed, Embase, and CENTRAL. Included studies compared DOACs to VKAs for the treatment of LV thrombi and reported on relevant outcomes. Odds ratios (ORs) were pooled with a random-effects model. RESULTS: = 56%). CONCLUSIONS: Within the context of low-quality evidence, there was a statistically significant reduction in stroke among those treated with DOACs, without an increase in bleeding. There were no significant differences in systemic embolism, stroke or systemic embolism, mortality, or LV thrombus resolution, suggesting that DOACs may be a reasonable option for treatment of LV thrombi.
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.006 | 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) | 0.005 | 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".