An evaluation of CardioPrevent
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cardiovascular diseases (CVDs) are the leading cause of mortality globally. Primary CVD prevention programs have the potential to improve risk factor profiles and, ultimately, the risk of developing CVD. The present study presents an evaluation of CardioPrevent, a global cardiovascular risk reduction program. RECENT FINDINGS: Of the 478 participants enrolled in the CardioPrevent program, 308 and 236 had complete 6-month and 12-month data, respectively at the time of evaluation. At 6 months, the average reduction in the Framingham risk score was -19.5% (median = -26.5%). Women experienced a greater reduction in risk than men (-23.1 vs. -11.4%, P = 0.013). Significant improvements were observed in body composition, blood pressure, low-density lipoproteins, triglycerides, total cholesterol-to-high-density lipoprotein ratio, HbA1c, perceived stress, anxiety, depression, quality of life, physical activity, sitting time, fruit and vegetable consumption, and medication adherence. Improvements seen at 6 months were maintained at 12 months. The majority (98%) of participants were very satisfied with the program and would recommend it to others. SUMMARY: Results of this evaluation identified that CardioPrevent is an effective CVD risk reduction program with high satisfaction rates. CardioPrevent is an effective, scalable program with the capacity to reduce CVD risk among primary care patients.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".