Changes in quality of life, cognition and functional status following catheter ablation of atrial fibrillation
Bibliographic record
Abstract
OBJECTIVE: To investigate changes in quality of life (QoL), cognition and functional status according to arrhythmia recurrence after atrial fibrillation (AF) ablation. METHODS: We compared QoL, cognition and functional status in patients with recurrent atrial tachycardia (AT)/AF versus those without recurrent AT/AF in the AXAFA-AFNET 5 clinical trial. We also sought to identify factors associated with improvement in QoL and functional status following AF ablation by overall change scores with and without analysis of covariance (ANCOVA). RESULTS: -VASc scores (2.8 vs 2.3, p<0.001) and more persistent forms of AF (51 vs 39%, p=0.012). Median changes in the SF-12 physical (3 (25th, 75th: -1, 8) vs 1 (-5, 8), p=0.026) and mental scores (2 (-3, 9) vs 0 (-4, 5), p=0.004), EQ-5D (0 (0,2) vs 0 (-0.1, 0.1), p=0.027) and Karnofsky functional status scores (10 (0, 10) vs 0 (0, 10), p=0.001) were more favourable in patients without recurrent AT/AF. In the overall cohort, the proportion with at least mild cognitive impairment (Montreal Cognitive Assessment <26) declined from 30.3% (n=157) at baseline to 21.8% (n=113) at follow-up. ANCOVA identified greater improvement in Karnofsky functional status (p<0.001) but not SF-12 physical (p=0.238) or mental scores (p=0.065) in those without recurrent AT/AF compared with patients with recurrent AT/AF. CONCLUSIONS: Patients without recurrent AT/AF appear to experience greater improvement in functional status but similar QoL as those with recurrent AT/AF after AF ablation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".