Can Cardiac Resynchronization Obviate the Need for a Heart Transplant? A Peak Oxygen Uptake Perspective
Bibliographic record
Abstract
Ventricular resynchronization therapy (VRT) (left ventricular or bi-ventricular stimulation) has been recently explored as an adjunct therapy to drugs to treat symptoms of heart failure (HF) in patients with conduction delay.Although long term improvement in functional capacity has been reported with VRT, the dependence of benefit on etiology of heart failure, if any, is unclear.Our goal was to test the effect of ischemic (ICM) or non-ischemic (NICM) etiologies in HF patients on long term benefit achieved with VRT.Methods: Twenty-five patients (Age: 60 f 4 years, QRS: 171 z!= 25 ms, NYHA class 3.1 f 0.2, EF: 21 f 6%,) enrolled in the PATH-CHF study were included for analysis.18 patients had heart failure from NICM and 7 patients due to ICM.The two groups were well matched for age, NYHA class and EF.The patients were stimulated for a period of 6 months with a one-month non-paced period from 4-8 weeks.Six-minute walk distance (6'WD) and quality of life score (QOL) were evaluated before implant and at the end of 6 months of follow-up.Two-tailed paired t-test analyses were done to compare the changes in 6'WD and QOL in both groups.Results: 6'WD and QOL data was missing from one patient in the NICM and ICM group respectively.I-IF patients with both NICM and ICM etiologies significantly benefit with VRT.The degree of benefit between the two groups is not different for 6'WD (P = 0.28) or QGL (P = 0.50)..-..-,q '-#a ,m Q 5-.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".