Differences in Perceived and Predicted Bleeding Risk in Older Adults With Atrial Fibrillation: The SAGE‐AF Study
Bibliographic record
Abstract
Background Little research has evaluated patient bleeding risk perceptions in comparison with calculated bleeding risk among oral anticoagulant users with atrial fibrillation. Our objective was to investigate underestimation of bleeding risk and to describe the characteristics and patient‐reported outcomes associated with underestimation of bleeding risk. Methods and Results In the SAGE‐AF (Systematic Assessment of Geriatric Elements in Atrial Fibrillation) study, a prospective cohort study of patients ≥65 years with atrial fibrillation, a CHA2DS2‐VASc risk score ≥2 and who were on oral anticoagulant therapy, we compared patients’ self‐reported bleeding risk with their predicted bleeding risk from their HAS‐BLED score. Among the 754 participants (mean age 74.8 years, 48.3% women), 68.0% underestimated their bleeding risk. Participants who were Asian or Pacific Islander, Black, Native American or Alaskan Native, Mixed Race or Hispanic (non‐White) (adjusted OR [AOR], 0.45; 95% CI, 0.24–0.82) and women (AOR, 0.62; 95% CI, 0.40–0.95) had significantly lower odds of underestimating their bleeding risk than respective comparison groups. Participants with a history of bleeding (AOR, 3.07; 95% CI, 1.73–5.44) and prior hypertension (AOR, 4.33; 95% CI, 2.43–7.72), stroke (AOR, 5.18; 95% CI, 1.87–14.40), or renal disease (AOR, 5.05; 95% CI, 2.98–8.57) had significantly higher odds of underestimating their bleeding risk. Conclusions We found that more than two‐thirds of patients with atrial fibrillation on oral anticoagulant therapy underestimated their bleeding risk and that participants with a history of bleeding and several comorbid conditions were more likely to underestimate their bleeding risk whereas non‐Whites and women were less likely to underestimate their bleeding risk. Clinicians should ensure that patients prescribed oral anticoagulant therapy have a thorough understanding of bleeding risk.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".