Atorvastatin can ameliorate left atrial stunning induced by radiofrequency ablation for atrial fibrillation
Bibliographic record
Abstract
The objective of this study was to study the functional changes of the left atrium after radiofrequency ablation treatment for atrial fibrillation and the therapeutic effect of atorvastatin. Fifty-eight patients undergoing radiofrequency ablation for atrial fibrillation were randomly divided into non-atorvastatin group and atorvastatin group. Patients in the atorvastatin group were treated with atorvastatin 20 mg p.o. per night in addition to the conventional treatment of atrial fibrillation; patients in the non-atorvastatin group received conventional treatment of atrial fibrillation only. Echocardiography was performed before radiofrequency ablation operation and 1 week, 2 weeks, 3 weeks, and 4 weeks after operation. Two-dimensional ultrasound speckle tracking imaging system was used to measure the structural indexes of the left atrium. Results indicated that there was no significant change for indexes representing the structural status of the left atrium within a month after radiofrequency ablation (P > 0.05); however, there were significant changes for indexes representing the functional status of the left atrium. There were also significant changes in indexes reflecting left atrial strain status: the S and SRs of atorvastatin group were higher than those of non-atorvastatin group (P < 0.05). In summary, atorvastatin could improve left atrial function and shorten the duration of atrial stunning after radiofrequency ablation of atrial fibrillation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".