P3582Increased thromboprophylactic treatment of patients with atrial fibrillation after the introduction of NOACs - an analysis of sex and gender differences
Bibliographic record
Abstract
Background: Anticoagulants have been underused for stroke prevention in patients with atrial fibrillation (AF), especially among women. With the introduction of non-vitamin-K oral anticoagulants (NOACs) a higher use of anticoagulants was anticipated. Purpose: To examine sex differences in thromboprophylaxis among patients with non-valvular AF before and after the introduction of NOACs. Methods: Repeated cross-sectional registry study of all individuals with a diagnosis of non-valvular AF in the region of Stockholm, Sweden (2.2 million inhabitants) in 2011 and 2015, respectively. CHA2DS2-VASc was used for risk scoring and dispensed warfarin, low-dose acetylsalicylic acid (ASA) and NOACs were studied. Results: During 2007–2011, 23,198 men and 18,504 women had an AF diagnosis. In 2011, more men than women (53% vs. 48%) received oral anticoagulants (almost exclusively warfarin) and more women received ASA only (35% vs. 30%) while there was no sex difference for no thromboprophylaxis (17%). During 2011–2015, there were 27,237 men and 20,461 women with a diagnosis of AF. Compared to the earlier time period a higher proportion used oral anticoagulants. In 2015, 75 percent of patients with CHA2DS2VASc ≥2 claimed an oral anticoagulant. Men and women were dispensed anticoagulants equally often (men 70% and women 71%) but fewer women ≥80 years received anticoagulants, more women received ASA (15% vs. 13%), and fewer women had no thromboprophylaxis (15% vs. 17%). Patients with comorbidities potentially complicating oral anticoagulant use utilized more oral anticoagulants in 2015 compared to 2011.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".