Sex differences in catheter ablation of atrial fibrillation: results from AXAFA-AFNET 5
Bibliographic record
Abstract
AIMS: Study sex-differences in efficacy and safety of atrial fibrillation (AF) ablation. METHODS AND RESULTS: We assessed first AF ablation outcomes on continuous anticoagulation in 633 patients [209 (33%) women and 424 (67%) men] in a pre-specified subgroup analysis of the AXAFA-AFNET 5 trial. We compared the primary outcome (death, stroke or transient ischaemic attack, or major bleeding) and secondary outcomes [change in quality of life (QoL) and cognitive function] 3 months after ablation. Women were older (66 vs. 63 years, P < 0.001), more often symptomatic, had lower QoL and a longer history of AF. No sex differences in ablation procedure were found. Women stayed in hospital longer than men (2.1 ± 2.3 vs. 1.6 ± 1.3 days, P = 0.004). The primary outcome occurred in 19 (9.1%) women and 26 (6.1%) men, P = 0.19. Women experienced more bleeding events requiring medical attention (5.7% vs. 2.1%, P = 0.03), while rates of tamponade (1.0% vs. 1.2%) or intracranial haemorrhage (0.5% vs. 0%) did not differ. Improvement in QoL after ablation was similar between the sexes [12-item Short Form Health Survey (SF-12) physical 5.1% and 5.9%, P = 0.26; and SF-12 mental 3.7% and 1.6%, P = 0.17]. At baseline, mild cognitive impairment according to the Montreal Cognitive Assessment (MoCA) was present in 65 (32%) women and 123 (30%) men and declined to 23% for both sexes at end of follow-up. CONCLUSION: Women and men experience similar improvement in QoL and MoCA score after AF ablation on continuous anticoagulation. Longer hospital stay, a trend towards more nuisance bleeds, and a lower overall QoL in women were the main differences observed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".