Trends of use and factors that determine the choice of oral anticoagulants in women and men with atrial fibrillation
Bibliographic record
Abstract
Abstract The aim was to identify sex‐specific factors linked with oral anticoagulant initiation in a cohort of patients with atrial fibrillation using administrative data from Quebec (Canada) between 2014 and 2017. Cohort entry defined as new users, that is, no claims in last 12 months, a cohort of 32 050 patients was stratified in two groups, that is, women and men. Multivariable regression models were used to identify factors of initiations for low‐ and standard‐dose direct oral anticoagulants (DOACs) versus warfarin, and low‐ versus standard‐dose DOACs. In both sexes, warfarin initiation decreased and DOAC initiation increased, with year of initiation as major factors of DOACs use. In 2017, the increase was of 2‐ to 4‐fold and 3‐ to 8‐fold for low‐ and standard‐dose DOACs (vs. warfarin), respectively. The proportion of patients starting on a low‐dose DOAC was higher in women than men. Older age for both sexes and CHADS2 score ≥2 (only women) were major factors of low‐dose dabigatran and rivaroxaban versus warfarin use. The only significant factor of standard‐dose DOAC versus warfarin use was age of 65–79 for women or men treated with apixaban by 1.8‐ and 1.4‐fold, respectively. Factors that made women and men less likely to receive a standard‐dose DOAC versus warfarin were higher CHADS2 (for dabigatran and rivaroxaban), HAS‐BLED and frailty scores, prior coronary disease, major bleeding, and chronic kidney disease (CKD) status. The choice of a low‐ versus standard‐dose DOAC was mainly driven by age and CKD, and higher CHADS2 score (for dabigatran and apixaban) for both sexes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".