Effects of Ablation Versus Drug Therapy on Quality of Life by Sex in Atrial Fibrillation: Results From the CABANA Trial
Bibliographic record
Abstract
Background Women with atrial fibrillation (AF) demonstrate more AF-related symptoms and worse quality of life (QOL). Whether increased use of ablation in women reduces sex-related QOL differences is unknown. Sex-related outcomes for ablation versus drug therapy was a prespecified analysis in the CABANA (Catheter Ablation Versus Antiarrhythmic Drug Therapy for Atrial Fibrillation) trial. Methods and Results Symptoms were assessed periodically over 60 months with the Mayo AF-Specific Symptom Inventory (MAFSI) frequency score, and QOL was assessed with the Atrial Fibrillation Effect on Quality of Life (AFEQT) summary and component scores. Women had lower baseline QOL scores than men (mean AFEQT scores 55.9 and 65.6, respectively). Ablation patients improved more than drug therapy patients with similar treatment effect by sex: AFEQT 12-month mean adjusted treatment difference in women 6.1 points (95% CI, 3.5-8.6) and men 4.9 points (95% CI, 3.0-6.9). Participants with baseline AFEQT summary scores <70 had greater QOL improvement, with a mean treatment difference at 12 months of 7.6 points for women (95% CI, 4.3-10.9) and 6.4 points for men (95% CI, 3.3-9.4). The mean adjusted difference in MAFSI frequency score between women randomized to ablation versus drug therapy at 12 months was -2.5 (95% CI, -3.4 to -1.6); for men, the difference was -1.3 (95% CI, -2.0 to -0.6). Conclusions Compared with drug therapy for AF, ablation resulted in more QOL improvement in both sexes, primarily driven by improvements in those with lower baseline QOL. Ablation did not eliminate the AF-related QOL gap between women and men. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT00911508.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".