Paroksismal Atrial Fibrilasyonlu Hastalarda Strokun Transözefagial Ekokardiyografik Prediktörleri
Bibliographic record
Abstract
Objective The left atrial appendage is the most source of thrombus formation in atrial fibrillation.The aim of this study was to find out left atrial appendage functions in patients with PAF with or without stroke. Materials and Methods This study included 74 paroxysmal atrial fibrillation patients who were performed transesophageal echocardiography for evaluation of stroke or who have suspicion doubt of atrial septal defect and patent foramen ovale. All patients had undergone 24 hours electrocardiography Holter recorder, 2 dimensional echocardiography, transesophageal echocardiography. Results Ther e are no differences between the groups for diabetes, hypertension, smoking, hyperlipidemia and creatinin e levels. Patients with stroke gr o up had lower left atrial appendage filling velocity (26.8 ± 5, 38.9 ±5 ) (p<0.001), had lower left atrial appendage contraction velocity (30.8 ± 6, 46.6 ±7 ) (p<0.001) and had bigger left atrial appendage area (2.7 ± 0.6, 2.4 ±0.4 ) (p=0.03) than without stroke group. Left atrial appendage contraction velocity (p=0.013) and filling velocity (p=0.045) are the independent predictors of stroke. Conclusion Our findings showed that stroke is associated with low filling velocity and low contraction velocity of left atrial appendage . Our findings suggest that this indices are independent predictors of stroke. If these results are confirmed in future studies, patients with paroxysmal atrial fibrillation without stroke and with low filling velocity and low contraction velocity of left atrial appendage should receive more medical attention, to reduce unfavorable outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".