Evaluating efficacy and safety of oral anticoagulation in adult patients with atrial fibrillation and cancer: A systemic review and meta-analysis
Bibliographic record
Abstract
Atrial fibrillation (AF) is common among patients with cancer. Patients with cancer and AF require anticoagulant therapy [direct oral anticoagulants (DOAC) or vitamin K antagonist (VKA)] for stroke and systemic embolism (SE) prevention. We sought to assess the rates of stroke/SE and major bleeding in patients with cancer and AF on oral anticoagulant therapy (DOAC or VKA). A systematic search of MEDLINE and EMBASE was conducted. The primary efficacy and safety outcome were stroke/SE and major bleeding (as per the International Society on Thrombosis and Haemostasis definition), respectively. Incidence rates (IR) were pooled using random effects model (event per 100 patient-years). Incidence rate ratios (IRR) were computed using a Poisson regression model with associated 95% confidence intervals (CI) using R software (version 4.0.3). Of the total 2,153 article records that were screened, 22 observational studies from 12 different countries were included in the meta-analysis (n = 94,980 patients). The IR of stroke/SE was 1.81 (95% CI: 0.89 to 3.68) and 3.41 (95% CI: 1.38 to 8.41) per 100 patient-years for patients receiving a DOAC and VKA, respectively (IRR: 0.63 (95%CI: 0.47–0.84)). The IR of major bleeding was 2.59 (95%CI: 1.54 to 4.38) and 3.60 (95% CI: 1.68 to 7.71) per 100 patient-years for patients receiving a DOAC and VKA, respectively (IRR: 0.76 (95% CI: 0.55 to 1.04)). DOACs compared to VKA seem to provide a significant reduction in the risk of stroke/SE and a good risk-benefit ratio profile for safety outcomes in this patient population.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".