Optimizing primary care management of atrial fibrillation: The rationale and methods of the Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF) study
Bibliographic record
Abstract
The Integrated Management Program Advancing Community Treatment of Atrial Fibrillation (IMPACT-AF) is an investigator designed, prospective, randomized, un-blinded, cluster design clinical trial, conducted in the primary care setting of Nova Scotia, Canada. Its aim is to evaluate whether an electronic Clinical Decision Support System (CDSS) designed to assist both practitioners and patients with evidence-based management strategies for Atrial Fibrillation (AF) can improve process of care and outcomes in a cost-efficient manner as compared to usual AF care. At least 200 primary care providers are being recruited and randomized at the level of the practice to control (usual care) or intervention (eligible to access to CDSS) cohorts. Over 1,000 patients of participating providers with confirmed AF will be managed per their provider's respective assignment. The targeted primary clinical outcome is a reduction in the composite of unplanned cardiovascular (CV) or major bleeding hospitalizations and AF-related emergency department visits. Secondary clinical outcomes, process of care, patient and provider satisfaction as well as economic costs at the system and patient levels are being examined. The trial is anticipated to report in 2018.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".