Common Carotid Artery Intima-Media Thickness: The Cardiovascular Risk Factor Multiple Evaluation in Latin America (CARMELA) Study Results
Bibliographic record
Abstract
BACKGROUND: Measurement of far wall common carotid artery intima-media thickness (CCAIMT) has emerged as a predictor of incident cardiovascular events. The Cardiovascular Risk Factor Multiple Evaluation in Latin America (CARMELA) study was the first large-scale population-based assessment of both CCAIMT and cardiovascular risk factor prevalence in 7 Latin American cities; the relationship between CCAIMT and cardiovascular risk markers was assessed in these urban Latin American centers. METHODS: CARMELA was a cross-sectional, population-based, observational study using stratified, multistage sampling. The participants completed a questionnaire, were evaluated in a clinical visit and underwent carotid ultrasonography. Clinical measurements were obtained by health personnel trained, certified and supervised by CARMELA investigators. Mannheim intima-media thickness consensus guidelines were followed for measurement of CCAIMT. RESULTS: In all cities and for both sexes, CCAIMT increased with higher age. CCAIMT was greater in the presence of cardiovascular risk factors than in their absence. In all cities, there was a statistically significant linear trend between increasing CCAIMT and a growing number of cardiovascular risk factors (p < 0.001). After adjustment for age and sex, metabolic syndrome was strongly associated with increased CCAIMT (p < 0.001 in all cities), as were hypercholesterolemia, obesity and diabetes (p < 0.001 in most cities). By multivariate analysis, hypertension was independently associated with an increase in CCAIMT in all cities (p < 0.01). CONCLUSIONS: CARMELA was the first large-scale population study to provide normal CCAIMT values according to age and sex in urban Latin American populations and to show CCAIMT increases in the presence of cardiovascular risk factors and metabolic syndrome.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".