Baseline Quality of Life Can Predict Improvement of Metabolic Equivalents Following Cardiac Rehabilitation Program
Bibliographic record
Abstract
The determining role of physical and psychological components of quality of life (QOL) for predicting cardiac rehabilitation (CR) physical and metabolic outcome is already questioned. current study evaluated the pivotal role of baseline QOL to predict the changes of METs as a main improvable physical parameter following CR. A total of 277 patients who underwent coronary artery bypass surgery (CABG) (n = 215) and percutaneous coronary intervention (PCI) (n = 62) and participated consecutively in 8-week CR program were evaluated. The SF-36 questionnaire and its physical and mental summary scores were proposed for assessing the patients' QOL. METs value was measured based on the stress test results at the day of admission and also at the conclusion of the program. No significant differences were found between the baseline physical and mental summary scores between men and women. However, those who underwent PCI had significantly higher mental summary score as well as total score of SF-36 compared to the participants undergoing pure CABG. Multivariable analysis indicated a strong positive correlation of METs improvement with both physical mental summary scores. QOL following cardiac procedures has a pivotal role to predict METs value during CR program and therefore can effectively determine outcome of CR, especially metabolic improvement in patients undergoing cardiac procedures.
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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 | 0.003 |
| 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) | 0.002 | 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".