C-Reactive Protein as a Screening Test for Cardiovascular Risk in a Multiethnic Population
Bibliographic record
Abstract
BACKGROUND: Small increases in the inflammatory marker C-reactive protein (CRP) are predictive of vascular events among asymptomatic individuals. There are few data supporting the use of CRP as a risk marker among nonwhite individuals. METHODS AND RESULTS: 1250 adults of South Asian, Chinese, European, and Aboriginal ancestry were randomly sampled from 4 communities in Canada. Participants provided fasting blood samples for CRP, glucose, lipids, and coagulation factors, and they had undergone a carotid B-mode ultrasound. Cardiovascular disease was determined by history and electrocardiogram. The age- and sex-adjusted mean CRP was 3.74 mg/L (standard error, 0.14) among Aboriginals, 2.59 mg/L (0.12) among South Asians, and 1.18 mg/L (0.13) among Chinese compared with 2.06 mg/L (0.12) among Europeans (overall P<0.0001). Differences in the CRP concentration between ethnic groups were substantially diminished, but not abolished, after adjustment for metabolic factors. CRP was independently associated with CVD after adjusting for the Framingham risk factors, atherosclerosis, anthropometric measurements, and ethnicity (OR=1.03 for a 0.1-increase in CRP; P=0.02). CONCLUSIONS: CRP varies substantially between people of different ethnic origin and is influenced by their differences in metabolic factors. Prospective validation of CRP as a risk predictor for cardiovascular disease among nonwhite ethnic groups is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".