Prognostic values of modifiable risk factors for cardiovascular events in South African health promotion
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases (CVDs) are increasing at an alarming rate among the South African population. This study aimed to determine the prognostic value of modifiable CVD risk factors for fatal and non-fatal events to inform cardiovascular health promotion practices in the South African public health system. METHODS: Data was collected from individuals participating in the South African leg of a multi-national prospective cohort study. Binary logistic regression was applied to estimate odds of total, non-fatal and fatal cardiovascular events. RESULTS: Binary logistic regression analyses identified age as a predictor of non-fatal and fatal CV events, with ORs of 1.87 to 3.21, respectively. Hypertension increased the odd of suffering a non-fatal CV event by almost two and a half (OR = 2.47; 95% CI = 1.26, 4.85). Moreover, being physically active reduced the odd of non-fatal CVD events by 38% (OR = 0.62; 95% CI = 0.46, 0.83 for 1 Standard deviation increase of the weighted physical activity index score (WPA)). On the one hand, gamma-glutamyltransferase (GGT) was associated with a higher fatal cardiovascular disease risk OR = 2.45 (95% CI = 1.36, 4.42) for a standard deviation increase. CONCLUSIONS: Elevated blood pressure, GGT, and physical activity have significant prognostic values for fatal or non-fatal CV events. These findings emphasise the importance of highlighting hypertension and physical activity when planning cardiovascular health education and intervention programmes for this population, with attention to the monitoring of GGT.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".