Development and Validation of the Atrial Fibrillation Effect on QualiTy-of-Life (AFEQT) Questionnaire in Patients With Atrial Fibrillation
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Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) has a deleterious impact on health-related quality-of-life (HRQoL), but measuring this outcome is difficult. A comprehensive, validated, disease-specific questionnaire to measure the spectrum of QoL domains affected by AF and its treatment is not available. We developed and validated a 20-item questionnaire, Atrial Fibrillation Effect on QualiTy-of-life (AFEQT), in a 6-center, prospective, observational study. METHODS AND RESULTS: Factor analyses established 4 conceptual domains (Symptoms, Daily Activities, Treatment Concern, and Treatment Satisfaction) from which individual domain and global scores were calculated. Participants from 6 centers completed the AFEQT at baseline, at month 1, and at month 3. Psychometric analyses included internal consistency and known-group validity. Test-retest reliability was assessed by comparing 1-month changes in scores among those with no change in therapy. Effect size was used to assess responsiveness after intervention. Among 219 patients age 62±11.9 years, 94% completed the AFEQT at baseline and 3 months; 66% had paroxysmal, 24% persistent, 5% longstanding persistent, and 5% permanent AF. Internal consistency was >0.88 for all scales. Lower AFEQT scores were observed with increased AF severity, categorized as asymptomatic, mild, moderate and severe, respectively: 71.2±20.6, 71.3±19.2, 57.9±19.0, and 42.0±21.2. Intraclass correlations for Overall, Symptoms, Daily Activities, Treatment Concern, and Satisfaction scores were 0.8, 0.5, 0.8, 0.7, and 0.7, respectively. Changes in 3-month scores were larger after ablation than with pharmacological adjustments, and both were greater than those observed in stable patients. CONCLUSIONS: This initial validation of AFEQT supports its use as an outcome in studies and a means to clinically follow patients with AF.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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