Predictors of Metabolic Syndrome in Participants of a Cardiac Rehabilitation Program
Bibliographic record
Abstract
Metabolic syndrome increases the risk of all-cause mortality, cardiovascular mortality and cardiovascular events in patients with cardiovascular disease (CVD). This study assessed the predictors of metabolic syndrome, both its incidence and resolution in a cohort of cardiac rehabilitation program graduates. Methods. A total of 154 and 80 participants without and with metabolic syndrome respectively were followed for 48 months. Anthropometric measurements, metabolic risk factors, and quality of life were assessed at baseline and at 48 months. Logistic regression models were used to assess the predictors of metabolic syndrome onset and resolution. Results. Increasing waist circumference (OR 1.175, P ≤ 0.001) was an independent predictor for incident metabolic syndrome (R(2) for model = 0.46). Increasing waist circumference (OR 1.234, P ≤ 0.001), decreasing HDL-C (OR 0.027, P = 0.005), and increasing triglycerides (OR 3.005, P = 0.003) were predictors of metabolic syndrome resolution. Conclusion. Patients with CVD that further develop metabolic syndrome are particularly susceptible for the cascade of cardiovascular events and mortality. Increasing waist circumference confers a higher risk for future onset of metabolic syndrome in this group of patients. They will require closer follow-up and should be targeted for further prevention strategies after cardiac rehabilitation program completion.
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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 | 0.002 |
| 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 | 0.001 |
| 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".