Atrial Fibrillation and Congestive Heart Failure: Specific Considerations at the Intersection of Two Common and Important Cardiac Disease Sets
Bibliographic record
Abstract
Atrial fibrillation (AF) and congestive heart failure (CHF) are two increasingly common cardiac disorders with a growing prevalence in the overall population. Improved treatment of acute medical conditions has increased the incidence of these cardiac disorders. AF and CHF have similar epidemiologic characteristics and adversely affect quality of life and life expectancy of affected patients. CHF predisposes to AF, and AF may worsen the prognosis of CHF. The relevant literature was intensively reviewed with emphasis on aspects at the intersection of both disease sets. Recent advances in basic research have provided a more in-depth view of changes promoting the occurrence of AF in CHF. Data from clinical trials have provided means to improve medical treatment of AF. Precautions must be taken for specific CHF-related side effects, such as torsades de pointes tachycardia, when treating AF. The specific electrophysiologic basis of AF associated with CHF may provide targets for improved treatment modalities. New treatment approaches, both pharmacologic and nonpharmacologic, as well as the results of ongoing controlled clinical studies are likely to greatly alter AF therapy over the next 5 to 10 years in patients with CHF.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".