Bibliographic record
Abstract
PURPOSE OF REVIEW: Heart failure and atrial fibrillation are both common cardiac conditions that share multiple risk factors. Heart failure is a risk for atrial fibrillation and atrial fibrillation is a risk for heart failure. The need to understand the interplay between these two cardiac conditions and the effectiveness of management options becomes increasingly relevant. RECENT FINDINGS: Recent studies have focused on the prognostic nature of atrial fibrillation and heart failure, the questionable utility of digoxin and beta-blocker therapy when heart failure and atrial fibrillation coexist, and the efficacy of cardiac ablation and resynchronization therapy with concomitant heart failure and atrial fibrillation. SUMMARY: The predominant questions that require further attention with respect to atrial fibrillation and heart failure are whether catheter ablation and rhythm control offers benefit in a high-risk heart failure population with respect to mortality or heart failure reduction, and whether cardiac resynchronization therapy implantation truly benefits the subgroup of candidate patients with permanent atrial fibrillation. Large randomized multicentre studies are currently ongoing to address these important questions.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".