Left Ventricular Volume Reduction by Radiofrequency Heating of Chronic Myocardial Infarction in Patients With Congestive Heart Failure
Bibliographic record
Abstract
BACKGROUND: Myocardial infarct expansion and left ventricular (LV) remodeling are integral components in the evolution of chronic heart failure and predict morbidity and mortality. Radiofrequency (RF) heating and patch placement of chronic LV aneurysms caused a sustained reduction in LV infarct area and volume in an ovine infarct model. This study evaluated the effect of RF heating and epicardial patch as an adjunct to coronary artery bypass graft on LV volumes in patients with prior myocardial infarction, evidence of akinetic/dyskinetic scar, and LV ejection fraction < or =40%. METHODS AND RESULTS: Ten patients (3 female; mean age, 64+/-11 years) scheduled for coronary artery bypass graft were enrolled (Canadian Cardiovascular Society angina class 2.1+/-1.1; New York Heart Association class 3.1+/-0.5). Intraoperative digital photography demonstrated an acute 39% reduction in infarct area (n=5; P=0.01), and transesophageal ECGs demonstrated a 16% acute reduction in LV end-diastolic volumes (n=9; P=0.002) after RF treatment. There were no intraoperative or procedure-related postoperative complications, and during an average follow-up of >180 days, there have been no safety issues. All patients had complete relief of their angina and improvement in exercise tolerance. Serial transthoracic ECGs over the 6 months of follow-up after RF treatment demonstrated persistent reductions in LV end-diastolic volume (29%; P<0.0001) and LV end-systolic volume (37%; P<0.0001) with improved ejection fraction (P<0.02). CONCLUSIONS: RF heating and patch placement in these 10 patients resulted in acute reduction in infarct area and ventricular volumes that were maintained 180 days after procedure. This technique may reduce the incidence of congestive heart failure and mortality in these patients and warrants investigation in larger 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 | 0.000 | 0.001 |
| 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".