Decision-making interventions to stop the global atrial fibrillation-related stroke tsunami
Bibliographic record
Abstract
Atrial fibrillation affects 33.5 million people worldwide and its prevalence is expected to double by 2050 because of the aging population. Atrial fibrillation confers a 5-fold higher risk of ischemic stroke compared to sinus rhythm. We present our view of the role of shared medical decision-making to combat global underutilization of oral anticoagulation for stroke prevention in atrial fibrillation patients. Oral anticoagulation underuse is widespread as it is present within atrial fibrillation patients of all risk strata and in countries across all income levels. Reasons for oral anticoagulation underuse include but are probably not limited to poor risk stratification, over-interpretation of contraindications, and discordance between physician prescription preferences and actual administration. By comparing a catastrophic event to the consequences of atrial fibrillation related strokes, it may help physicians and patients understand the negative outcomes associated with oral anticoagulation under-utilization and the magnitude to which oral anticoagulations neutralize atrial fibrillation burden.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".