Quality of life in patients with atrial fibrillation: how to assess it and how to improve it
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most frequent cardiac rhythm disorder and presents a considerable public health burden that is likely to increase in the next decades due to the ageing population. Current management strategies focus on the heart rate and rhythm control, thromboembolism prevention, and treatment of underlying diseases. The concept of quality of life (QoL) has gained significant importance in recent years as an outcome measure in AF studies evaluating therapeutic interventions and as a relevant component of a comprehensive treatment plan. Quality of life is impaired in the majority of patients with AF, and both rate and rhythm control strategies show significant improvement in QoL measures in highly symptomatic patients. This article reviews generic and specialized instruments for measuring QoL in the context of AF, discusses their applications and limitations to integration in clinical practice, and addresses the potential of early therapy for improving QoL outcomes. The development and validation of new QoL assessment tools will have a central role in the advancement of therapies and treatment guidelines for AF.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".