Early management of atrial fibrillation to prevent cardiovascular complications
Bibliographic record
Abstract
Atrial fibrillation (AF) is generally considered a progressive disease, typically evolving from paroxysmal through persistent to 'permanent' forms, a process attributed to electrical and structural remodelling related to both the underlying disease and AF itself. Medical treatment has yet to demonstrate clinical efficacy in preventing progression. Large clinical trials performed to date have failed to show benefit of rhythm control compared with rate control, but these trials primarily included patients at late stages in the disease process. One possible explanation is that intervention at only an early stage of progression may improve prognosis. Evolving observations about the progressive nature of AF, along with the occurrences of major complications such as strokes upon AF presentation, led to the notion that earlier and more active approaches to AF detection, rhythm-reversion, and maintenance of sinus rhythm may be a useful strategy in AF management. Approaches to early and sustained rhythm control include measures that prevent development of the AF substrate, earlier catheter ablation, and novel antiarrhythmic drugs. Improved classifications of AF mechanism, pathogenesis, and remodelling may be helpful to enable patient-specific pathophysiological diagnosis and therapy. Potential novel therapeutic options under development include microRNA-modulation, heatshock protein inducers, agents that influence Ca(2+) handling, vagal stimulators, and more aggressive mechanism-based ablation strategies. In this review, of research into the basis and management of AF in acute and early settings, it is proposed that progression from paroxysmal to persistent AF can be interrupted, with potentially favourable prognostic impact.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".