Fibrinogen concentration and its role in CVD risk in black South Africans – effect of urbanisation
Bibliographic record
Abstract
The aim of this study was to investigate correlates of fibrinogen concentration in black South Africans, as well as its association with cardiovascular disease (CVD) risk and whether urbanisation influences this association. A total of 1,006 rural and 1,004 urban black South Africans from the PURE study were cross-sectionally analysed. The association of fibrinogen with CVD risk was determined by investigating the association of fibrinogen with other CVD risk markers as well as with predicted CVD risk using the Reynolds Risk score. The rural group had a significantly higher fibrinogen concentration than the urban group, despite higher levels of risk factors and increased predicted CVD risk in the urban group. Increased levels of CVD risk factors were, however, still associated with increased fibrinogen concentration. Fibrinogen correlated significantly, but weakly, with overall predicted CVD risk. This correlation was stronger in the urban than in the rural group. Multiple regression analysis showed that a smaller percentage of the variance in fibrinogen is explained by the traditional CVD risk factors in the rural than in the urban group. In conclusion, fibrinogen is weakly associated with CVD risk (predicted overall risk as well with individual risk factors) in black South Africans, and is related to the degree of urbanisation. Increased fibrinogen concentration, in black South Africans, especially in rural areas, is largely unexplained, and likely not strongly correlated with traditional CVD-related lifestyle and pathophysiological processes. This does, however, not exclude the possibility that once increased, the fibrinogen concentration contributes to future development of 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) | 0.000 | 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 | 0.000 |
| 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".