Symptoms and quality of life in patients with coexistent atrial fibrillation and atrial flutter
Bibliographic record
Abstract
AIMS: Atrial fibrillation (AF) and atrial flutter (AFL) are two of the most common atrial arrhythmias and often coexist. Many patients with AF or AFL are symptomatic, which impacts their quality of life (QoL). The purpose of this study was to determine whether coexistent AFL represents an added burden for AF patients. METHODS: All participants had a clinical examination and a resting ECG. Symptom burden and QoL at the baseline examination were compared between patients with and without coexistent AFL using multivariable adjusted regression models. RESULTS: Overall, 809 (20.6%) patients had a history of AFL. Patients with coexistent AFL more often had history of heart failure (28% vs 23%, p = 0.01), coronary artery disease (30% vs 26%, p = 0.007), failed therapy with antiarrhythmic drugs (44% vs 29%, p < 0.001), and more often underwent AF-related interventions (36% vs 17%, p < 0.001). They were more often symptomatic (70% vs 66%, p = 0.04) and effort intolerant (OR: 1.14; 95% CI: 1.01-1.28; p = 0.04). Documented AFL on the baseline ECG was associated with more symptoms (OR: 2.30; 95% CI: 1.26-4.20; p = 0.007). CONCLUSION: Our data indicates that patients with coexistent AF and AFL are more often symptomatic and report poorer quality of life compared to patients suffering from AF only.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".