Developing an Atrial Fibrillation Guideline Support Tool (AFGuST) for shared decision making
Bibliographic record
Abstract
OBJECTIVE: Patient values and preferences are an important component to decision making when tradeoffs exist that impact quality of life, such as tradeoffs between stroke prevention and hemorrhage in patients with atrial fibrillation (AF) contemplating anticoagulant therapy. Our objective is to describe the development of an Atrial Fibrillation Guideline Support Tool (AFGuST) to assist the process of integrating patients' preferences into this decision. MATERIALS AND METHODS: CHA2DS2VASc and HAS-BLED were used to calculate risks for stroke and hemorrhage. We developed a Markov decision analytic model as a computational engine to integrate patient-specific risk for stroke and hemorrhage and individual patient values for relevant outcomes in decisions about anticoagulant therapy. RESULTS: Individual patient preferences for health-related outcomes may have greater or lesser impact on the choice of optimal antithrombotic therapy, depending upon the balance of patient-specific risks for ischemic stroke and major bleeding. These factors have been incorporated into patient-tailored booklets which, along with an informational video, were developed through an iterative process with clinicians and patient focus groups. KEY LIMITATIONS: Current risk prediction models for hemorrhage, such as the HAS-BLED, used in the AFGuST, do not incorporate all potentially significant risk factors. Novel oral anticoagulant agents recently approved for use in the United States, Canada, and Europe have not been included in the AFGuST. Rather, warfarin has been used as a conservative proxy for all oral anticoagulant therapy. CONCLUSIONS: We present a proof of concept that a patient-tailored decision-support tool could bridge the gap between guidelines and practice by incorporating individual patient's stroke and bleeding risks and their values for major bleeding events and stroke to facilitate a shared decision making process. If effective, the AFGuST could be used as an adjunct to published guidelines to enhance patient-centered conversations about the anticoagulation management.
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How this classification was reachedexpand
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".