Comparative effectiveness and safety of oral anticoagulants for atrial fibrillation: A retrospective cohort study
Bibliographic record
Abstract
Oral anticoagulants (OACs) are high-priority medications, frequently used with clinically important benefit and serious harm. Our objective was to compare the safety and effectiveness of direct-acting oral anticoagulants (DOACs) versus warfarin in a population where anticoagulation management and DOACs were readily available. A retrospective cohort study of all adults living in British Columbia with a diagnosis of atrial fibrillation and a first prescription for an OAC was conducted. Co-primary outcomes were ischemic stroke and systemic embolism, and major bleeding. Secondary outcomes included a net clinical outcome composite and analysis of discontinuation, switching, and key subgroups. We estimated the effects of treatment using time-to-event models with high-dimensional propensity score adjustment to control confounding. After adjustment for prescribing bias, a cohort (n = 20,113, 43.8% female, mean age 72.4 years) with a mean follow-up of 18.1 months showed that patients taking warfarin tended to be poorer, sicker, and less likely to have a cardiologist prescriber. Outcome event rates were not significantly different for DOACs compared to warfarin [adjusted rate ratio of 1.15 (0.91, 1.46) for systemic embolism, 0.94 (0.82, 1.08) for major bleeding, and 0.98 (0.90, 1.06) for net clinical outcome]. Only the effect of age on net clinical outcome met our strict criteria for predicting which group might be superior. Switch of drug class was associated with increased risk of events (p < 0.003). In this population, we found no difference in important clinical outcomes between warfarin and DOACs. Switching compared to not switching was associated with harm.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".