Rate of Recovery of Left Ventricular Ejection Fraction in a Real-World Population of Patients Receiving a Wearable Cardioverter Defibrillator
Bibliographic record
Abstract
BACKGROUND: This study aimed to investigate the rate of early improvement in ejection fraction (EF) within 21 - 60 days among patients with cardiomyopathy who were provided with a wearable cardioverter defibrillator (WCD). METHODS: This was a retrospective study of patients who received a WCD at our institution to determine the rate of improvement in left ventricular EF (LVEF) to ≥ 35-40%. Among 990 patients who received a WCD during the study period, 101 had an echocardiogram performed during the subsequent 21 - 60 days. Patients were stratified according to their initial EF, as well as age, gender, number of heart failure medications, and ischemic vs. nonischemic cardiomyopathy. Multivariate logistic regression analysis was performed to assess the influence of these variables on the subsequent improvement in EF. RESULTS: There were 39 patients who had improvement in their EF to ≥ 35-40%. The only significant predictor of EF recovery was the initial EF. There was a direct correlation between initial EF category and the likelihood of improvement in EF. For every unit increase in initial EF category, the odds of improvement increased 1.73 times (95% confidence interval (CI): 1.22 - 2.45). Age (P = 0.20), gender (P = 0.10), ischemic cardiomyopathy (P = 0.40), and number of heart failure medications at the time of WCD placement (P = 0.26) were not significant predictors of improved LVEF. CONCLUSIONS: This study showed a rate of improvement in EF to ≥ 35-40% of 39% within 21 - 60 days of placement of a WCD among patients with both ischemic and nonischemic cardiomyopathy. The only significant clinical predictor of EF improvement was initial EF.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".