Cardiometabolic markers to identify cardiovascular disease risk in HIV-infected black South Africans
Bibliographic record
Abstract
BACKGROUND: The prevalence of HIV is the highest in sub-Saharan Africa; South Africa (SA) is one of the most affected countries with the highest number of adults living with HIV infection in the world. Besides the traditional risk factors for cardiovascular disease (CVD) in the general population, in people living with HIV there are specific factors - chronic inflammation, metabolic changes associated with the infection, therapy, and lipodystrophy - that potentially increase the risk for developing CVD. OBJECTIVE: This study proposes a screening discriminant model to identify the most important risk factors for the development of CVD in a cohort of 140 HIV-infected black Africans from the North West Province, SA. METHODS: Anthropometric measures, systolic blood pressure, diastolic blood pressure and the carotid-dorsalis pedis pulse wave velocity were determined. Blood was analysed to determine the levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol, triglycerides (TGs) and glucose. Partial least squares discriminant analysis was performed as a supervised pattern recognition method. Independent Student's t-tests were further employed to compare the means of risk factors on interval scales; for comparison of categorical risk factors between groups, chi2 tests were used. RESULTS: A TG:HDL-C ratio > or = 1.49, TC:HDL-C ratio > or = 5.4 and an HDL-C level < or = 0.76 mmol/l indicated CVD risk in this cohort of patients living with HIV. CONCLUSION: The results have important health implications for black Africans living with HIV as these lipid levels may be a useful indicator of the risk for CVD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".