A clinical decision aid for the selection of antithrombotic therapy for the prevention of stroke due to atrial fibrillation
Bibliographic record
Abstract
AIMS: The availability of new antithrombotic agents, each with a unique efficacy and bleeding profile, has introduced a considerable amount of clinical uncertainty with physicians. We have developed a clinical decision aid in order to assist clinicians in determining an optimal antithrombotic regime for the prevention of stroke in patients who are newly diagnosed with non-valvular atrial fibrillation. METHODS AND RESULTS: The CHA(2)DS(2)-VASc and HAS-BLED scoring systems were used to assess patients' baseline risks of stroke and major bleeding, respectively. The relative risks of stroke and major bleeding for each antithrombotic agent were then used to identify the agent associated with the lowest net risk. Individual patient factors such as the treatment threshold, bleeding ratio, and cost threshold modified the recommendations in order to generate a final recommendation. By considering both patient factors and clinical research concurrently, this clinical decision aid is able to provide specific advice to clinicians regarding an optimal stroke prevention strategy. The resulting treatment recommendation tables are consistent with the recommendations of the European Society of Cardiology and Canadian Cardiovascular Society Guidelines, which can be incorporated into either a paper-based or electronic format to allow clinicians to have decision support at the point of care. CONCLUSION: The use of a clinical decision aid that considers both patient factors and evidence-based medicine will serve to bridge the knowledge gap and provide practical guidance to clinicians in the prevention of stroke due to atrial fibrillation.
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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 | 0.022 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".