Bibliographic record
Abstract
A trial fibrillation (AF) is a common clinical problem that is increasing in prevalence 1 and is inextricably linked to another burgeoning cardiovascular problem, namely, congestive heart failure. 2 There is increasing evidence that AF, at least in some population subsets, may be part of a spectrum of atherosclerotic vascular disease, hypertension, inflammation, diastolic dysfunction, and the metabolic syndromes.AF is part of a family of atrial tachyarrhythmias (Figure 1).A panel of experts has recently characterized the definition and position of AF within this group of tachyarrhythmias.3 Nonetheless, this family of tachyarrhythmias is closely interrelated, and the individual tachyarrhythmias often coexist in the same patient.Although the present discussion focuses on AF, many of the points made with regard to AF apply to these other tachyarrhythmias to varying degrees.Recently, international panels of experts have also created clinical practice guidelines 4 and perspectives on future research directions 5 for AF.These documents are a rich source of reference to the vast literature on AF.It is not the intention of the present article to review this literature, and, in particular, it is not intended that the reader will use the present article as a manual for managing AF.However, the perspective is intended to provide a framework for rational thinking about the management of AF.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".