Hypertension and cardiovascular disease endpoints by ethnic group: the promise of data linkage
Bibliographic record
Abstract
Hypertension is the most important risk factor for cardiovascular diseases (CVD), accounting for approximately 45% of global CVD morbidity and mortality.1 Evidence suggests striking differences in blood pressure (BP) and hypertension prevalence between ethnic groups. West African descent adults living in Europe and North America, whether they come directly from Africa or indirectly from the Caribbean, generally have higher BP levels and a higher prevalence of hypertension than European descent populations (henceforth, white individuals), with this being seen at all ages in North America and only from adulthood in the UK.2 ,3 Chinese-origin people also have slightly higher BP and prevalence of hypertension than white individuals.4 ,5 The evidence is mixed when it comes to the South-Asian descent populations (ie, Indian, Pakistani, Bangladeshi and Sri Lankan people). In a systematic review in the UK, BP levels among South-Asian individuals were generally similar to that of the UK general population, but there were stark differences among the South-Asian subgroups, with slightly higher BP in Indian individuals, slightly lower BP in Pakistani individuals, and much lower BP in Bangladeshi individuals.6 Studies in The Netherlands7 and Canada,5 ,8 however, show a higher hypertension prevalence in South-Asian than in white individuals. In the Ontario Health Survey, the age-standardised hypertension prevalence among South-Asian individuals was 30.1% compared with 20.7% among white Canadian people.8 South-Asian were still more likely than white Canadian individuals to have hypertension even after adjustment for age, sex and body mass index. While hypertension remains the most important risk factor for CVD, its contribution to the ethnic differences in CVD outcomes is still sometimes puzzling. In the UK, although the BP levels are similar or lower in the South-Asian relative to the general population, they have a higher mortality from stroke and …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".