Quality of life in patients with atrial fibrillation: how to assess it and how to improve it
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it