Age and Sex Specific Prevalence of Clinical and Screen-Detected Atrial Fibrillation in Hospitalized Patients
Bibliographic record
Abstract
Background: The prevalence of atrial fibrillation (AF) is high in older patients. The present study aimed to estimate the age and sex specific prevalence of clinical and screen-detected atrial fibrillation (AF) in hospitalized patients. Methods: The STAR-FIB cohort study was a prospective cohort study recruiting participants from a large source population of hospitalized patients aged 65–84 years. The estimated size of the source population was 26,035 (95% CI 25,918–26,152), and 795 consenting patients without clinical AF were included in the cohort study after stratification by sex and age (49.2% females; mean age 74.7 years). Patients in the cohort study underwent three seven-day Holter ECGs in intervals of two months to screen for AF. Results: In the source population, the estimated prevalence of clinical AF was 22.2% (95% CI 18.4–26.1), 23.8% for males (95% CI 20.9–26.6) and 19.8% for females (95% CI 17.3–22.4; p for difference between sexes, 0.004). There was a linear trend for an increase in the prevalence of clinical AF with increasing age, overall and in both sexes. In the cohort study, AF was newly diagnosed in 38 patients, for an estimated prevalence of screen-detected AF of 4.9% overall (95% CI 3.3–6.6), 5.5% in males (95% CI 3.2–7.8) and 4.0% in females (95% CI 2.0–6.0; p for difference between sexes, 0.041). The estimated prevalence of screen-detected AF in the source population was 3.8% overall, 4.2% in males and 3.2% in females. Conclusion: In a large hospital-based patient population aged 65–84 years, the prevalence of clinical AF and of screen-detected AF was 22.2% and 3.8%, respectively, and significantly higher in males than females.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".