Atrial fibrillation ablation in patients with heart failure review
Bibliographic record
Abstract
Atrial fibrillation and heart failure often coexist in patients with advanced heart failure symptoms. The result, in addition to a significant impact on quality of life, is an increase in the risk of a adverse clinical outcomes including stroke, hospitalization and overall mortality. Pharmacological therapy for atrial fibrillation in the heart failure population remains limited due to sub-optimal drug efficacy and a likely increased mortality due to pro-arrhythmia. Atrial fibrillation ablation, since it allows for therapy without the need for toxic medication, has the potential to become mainstream treatment in patients with drug refractory, symptomatic atrial fibrillation and heart failure. Randomized studies and observational data suggest that atrial fibrillation ablation provides superior rhythm control to anti-arrhythmic drugs. Atrial fibrilla- tion ablation is relatively safe and may result in improvement of left ventricular function and quality of life. Ongoing studies are attempting to assess a number of outcome measures to help define its role in the heart failure patient population. This review focuses on atrial fibrillation ablation in patients with congestive heart failure, and summarizes the results of available literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".