Physician decision making in anticoagulating atrial fibrillation: a prospective survey of a physician notification system for atrial fibrillation detected on cardiac implantable electronic devices of patients at increased risk of stroke
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to evaluate the effectiveness of a physician notification system for atrial fibrillation (AF) detected on cardiac devices, and to assess predictors of anticoagulation in patients with device-detected AF. METHODS: In 2013, a physician notification system for AF detected on a patient's CIED [including pacemakers, implantable cardioverter defibrillators (ICD) or cardiac resynchronization therapy (CRT) devices] was implemented, with a recommendation to consider oral anticoagulation in high-risk patients. We prospectively investigated the effectiveness of this system, and evaluated both patient and physician predictors of anticoagulation, as well as factors influencing physician decision making in prescribing anticoagulation. Both uni- and multivariable analysis as well as descriptive statistics were used in the analysis. RESULTS: was not a predictor of anticoagulation. ASA use predicted a lower rate of anticoagulation (OR 0.39, 95% CI 0.16-0.97, p = 0.04); physicians in practice for <20 years were more likely to prescribe anticoagulation (OR 3.39, 95% CI 1.28-8.93, p = 0.01); and physicians who believed both cardiologist and family doctor should be involved in managing anticoagulation were more likely to prescribe anticoagulation (OR 3.28, 95% CI 1.02-10.5, p = 0.05). CONCLUSIONS: Patients on aspirin were less likely to be anticoagulated. Physicians in practice for <20 years and who believed that both the general practitioner and cardiologist should be involved in managing anticoagulants were more likely to prescribe anticoagulation.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".