Prevention of Atrial Fibrillation
Bibliographic record
Abstract
The National Heart, Lung, and Blood Institute convened an expert panel April 28 to 29, 2008, to identify gaps and recommend research strategies to prevent atrial fibrillation (AF). The panel reviewed the existing basic scientific, epidemiological, and clinical literature about AF and identified opportunities to advance AF prevention research. After discussion, the panel proposed the following recommendations: (1) enhance understanding of the epidemiology of AF in the population by systematically and longitudinally investigating symptomatic and asymptomatic AF in cohort studies; (2) improve detection of AF by evaluating the ability of existing and emerging methods and technologies to detect AF; (3) improve noninvasive modalities for identifying key components of cardiovascular remodeling that promote AF, including genetic, fibrotic, autonomic, structural, and electrical remodeling markers; (4) develop additional animal models reflective of the pathophysiology of human AF; (5) conduct secondary analyses of already-completed clinical trials to enhance knowledge of potentially effective methods to prevent AF and routinely include AF as an outcome in ongoing and future cardiovascular studies; and (6) conduct clinical studies focused on secondary prevention of AF recurrence, which would inform future primary prevention investigations.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".