Improving stroke prevention therapy for patients with atrial fibrillation in primary care: protocol for a pragmatic, cluster-randomized trial
Bibliographic record
Abstract
BACKGROUND: The prevalence of atrial fibrillation (AF) is growing as the population ages, and at least 15% of ischemic strokes are attributed to AF. However, many high-risk AF patients are not offered guideline-recommended stroke prevention therapy due to a variety of system, provider, and patient-level barriers. METHODS: We will conduct a pragmatic, cluster-randomized controlled trial randomizing primary care clinics to test a "toolkit" of quality improvement interventions in primary care. In keeping with the recommendations of the chronic care model to simultaneously activate patients and facilitate proactive care by providers, the toolkit includes provider-focused strategies (education, audit and feedback, electronic decision support, and reminders) plus patient-directed strategies (educational letters and reminders). The trial will include two feedback cycles at baseline and approximately 6 months and a final data collection at approximately 12 months. The study will be powered to show a difference of 10% in the primary outcome of proportion of patients receiving guideline-recommended stroke prevention therapy. Analysis will follow the intention-to-treat principle and will be blind to treatment allocation. Unit of analysis will be the patient; models will use generalized estimating equations to account for clustering at the clinical level. DISCUSSION: Stroke prevention therapy using anticoagulation in patients with AF is known to reduce strokes by two thirds or more in clinical trials, but most studies indicate under-use of this treatment in real-world practice. If the toolkit successfully improves care for patients with AF, stakeholders will be engaged to facilitate broader application to maximize the potential to improve patient outcomes. The intervention toolkit tested in this project could also provide a model to improve quality of care for other chronic cardiovascular conditions managed in primary care. TRIAL REGISTRATION: ClinicalTrials.gov ( NCT01927445 ). Registered August 14, 2014 at https://clinicaltrials.gov/ .
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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 | 0.059 | 0.063 |
| Meta-epidemiology (narrow) | 0.009 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.010 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.076 | 0.013 |
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".