Sex-specific differences in adverse outcome events among patients with atrial fibrillation
Bibliographic record
Abstract
OBJECTIVE: To assess whether women with atrial fibrillation (AF) have a higher risk of adverse events than men during long-term follow-up since controversial data have been published. METHODS: In the context of two very similar observational multicentre cohort studies, we prospectively followed 3894 patients (28% women) with previously documented AF for a median of 4.02 (3.00-5.83) years. The primary outcome was a composite of ischaemic stroke, myocardial infarction and cardiovascular death. Secondary outcomes included the individual components of the composite outcome, hospitalisation for heart failure, major and clinically relevant non-major bleeding, stroke or systemic embolism and non-cardiovascular death. RESULTS: Mean age was 73.1 years in women vs 70.8 years in men. The incidence of the primary endpoint in women versus men was 2.46 vs 3.24 per 100 patient-years, respectively (adjusted HR (aHR) 0.74, 95% CI 0.58 to 0.94; p=0.01). Women died less frequently from cardiovascular (aHR 0.57, 95% CI 0.41 to 0.78; p<0.001) and non-cardiovascular causes (aHR 0.68, 95% CI 0.47 to 0.98; p=0.04). There were no significant sex-specific differences in stroke (incidence 1.05 vs 1.00; aHR 1.02, 95% CI 0.70 to 1.49, p=0.93), myocardial infarction (incidence 0.67 vs 0.72; aHR 0.98, 95% CI 0.61 to 1.57, p=0.94), major and clinically relevant non-major bleeding (incidence 4.51 vs 4.34; aHR 0.95, 95% CI 0.79 to 1.15, p=0.63) or heart failure hospitalisation (incidence 3.28 vs 3.07; aHR 1.06, 95% CI 0.85 to 1.32, p=0.60). CONCLUSION: In this large study of patients with established AF, women had a lower risk of death than men, but there were no sex-specific differences in other adverse outcomes.
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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| 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".