Approach to atrial fibrillation
Bibliographic record
Abstract
OBJECTIVE: To support family physicians in preventing atrial fibrillation (AF) in patients at risk and in identifying and managing those with established AF; and to summarize key recommendations for ideal screening and care of patients. SOURCES OF INFORMATION: The 2020 Canadian Cardiovascular Society and Canadian Heart Rhythm Society comprehensive guidelines for the management of AF, based on current evidence and clinical experience related to AF. MAIN MESSAGE: Atrial fibrillation, which is estimated to affect at least 500,000 Canadians, is associated with high risks of stroke, heart failure, and death. Primary care clinicians occupy a central role in the management of this chronic condition, focusing on the challenges of preventing AF and identifying, diagnosing, treating, and following patients with AF. Evidence-based guidelines that provide optimal management strategies have been published by the Canadian Cardiovascular Society and Canadian Heart Rhythm Society to assist in these tasks. Messages critical to primary care are offered to support effective knowledge translation. CONCLUSION: Most patients with AF can be managed effectively in primary care. Family physicians not only play an important role in ensuring patients with AF receive timely diagnoses, but they are also key to providing initial and ongoing care, especially in patients with comorbid conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".