A Standardized Bleeding Risk Score Aligns Anticoagulation Choices with Current Evidence
Bibliographic record
Abstract
OBJECTIVES: Atrial fibrillation (AF), the most common arrhythmia in elderly patients, accounts for 15% of strokes. Oral anticoagulation (OAC) can reduce the risk of stroke by 60% but is underprescribed. The HAS-BLED score (Hypertension, Abnormal renal or liver function, Stroke, Bleeding, Labile INR, Elderly, Drugs) can predict OAC bleeding complications. The authors hypothesized that use of HAS-BLED can help align decision making with current evidence. METHODS: The authors developed a survey with four clinical vignettes designed to highlight the complexity in deciding whether to anticoagulate elderly patients with AF. Physicians were randomly assigned to receive the survey either including the HAS-BLED score and the estimated annual risk of bleeding (intervention) or without (control). Following each vignette, participants were asked: (1) whether they would recommend OAC and (2) to estimate the risk of bleeding and stroke. The "appropriate" anticoagulation decision was defined as the choice that minimized the risk of stroke and major bleeding. RESULTS: A total of 203 physicians were recruited for the survey, with 55 responses obtained (27%). Physicians who were given the HAS-BLED score were 18% more likely to choose appropriate anticoagulation (74% vs. 56%, P < .05). The HAS-BLED score assisted physicians in both choosing to anticoagulate when appropriate and not to anticoagulate when the risk of bleeding outweighed the benefit. Overall, physicians were poor at estimating the risk of stroke (42% correct) and major bleeding (31% correct). CONCLUSIONS: Presentation of the HAS-BLED score led to an 18% improvement in appropriate OAC choices. Future study should evaluate incorporation of HAS-BLED use in real-time clinical situations.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".