Effectiveness and safety of atrial fibrillation ablation in females
Bibliographic record
Abstract
BACKGROUND: Existing data on the effectiveness and safety of atrial fibrillation (AF) ablation in females are limited to studies of small sample size, lacking longer term follow-up or adjustment for potential confounders. METHODS: A total of 6421 patients (2072 females) undergoing a first AF ablation procedure after enrollment in the Chinese Atrial Fibrillation Registry (China-AF) study between August 2011 and December 2017 were analyzed. We evaluated the effectiveness (recurrence of documented [symptomatic or not] atrial tachyarrhythmia (AT)) and the safety (incidence of procedure-related complications) of AF ablation in female patients compared to male patients. Sensitivity analyses based on routine data were also utilized to avoid potential sex differences in reporting of AF symptoms. RESULTS: Females were about 5 years older than males at the time of ablation (mean age 63.4 ± 9.5 vs 58.3 ± 10.8, P < .0001). A higher proportion of female patients had paroxysmal AF (74.3% vs 56.7%, P < .0001), hypertension (69.7% vs 61.3%, P < .0001), and hyperlipidemia (57.2% vs 52.9%, P = .001). Female sex was found to be an independent risk factor of AT recurrence in multivariate analyses (HR = 1.26, 95% CI 1.15-1.38, P < .0001). These findings were confirmed in sensitivity analyses using only Holter data. Female sex was also associated with a higher risk of periprocedural complications after adjustment for baseline variables (OR = 1.41, 95% CI 1.03-1.94, P = .03). CONCLUSIONS: Female sex is an independent risk factor of AT recurrence and periprocedural complications 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".