Left atrial volume and function in patients with atrial fibrillation
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review of emerging approaches to left atrial imaging in atrial fibrillation is relevant because there has been considerable recent development in the noninvasive characterization of left atrial structure and function. Concurrently, the identification and treatment of atrial fibrillation and the prevention of thromboembolism are evolving. Thus, it is timely to summarize how the advances in these two areas might be synergistic in the treatment of atrial fibrillation. RECENT FINDINGS: This article will summarize recent developments in left atrial imaging that play a role in patients with atrial fibrillation, with particular emphasis on echocardiography, and with reference made to important advances in cardiac computed tomography and cardiac magnetic resonance. The evidence that these modalities can predict who will develop atrial fibrillation, who will achieve sustained sinus rhythm after cardioversion or catheter ablation, and who will have thromboembolic risk will be reviewed. SUMMARY: Although existing evidence is promising, the clinical role of cardiac imaging to predict atrial fibrillation occurrence, atrial fibrillation recurrence after treatment, and thromboembolism from atrial fibrillation remains to be confirmed in large-scale studies and clinical trials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".