Screening for cardiovascular disease risk factors beginning in childhood
Bibliographic record
Abstract
Cardiovascular diseases (CVD) are the leading cause of death worldwide. Individual detection and intervention on CVD risk factors and behaviors throughout childhood and adolescence has been advocated as a strategy to reduce CVD risk in adulthood. The U.S. National Heart, Lung, and Blood Institute (NHLBI) has recently recommended universal screening of several risk factors in children and adolescents, at odds with several recommendations of the U.S. Services Task Force and of the U.K. National Screening committee. In the current review, we discuss the goals of screening for CVD risk factors (elevated blood pressure, abnormal blood lipids, diabetes) and behaviors (smoking) in children and appraise critically various screening recommendations. Our review suggests that there is no compelling evidence to recommend universal screening for elevated blood pressure, abnormal blood lipids, abnormal blood glucose, or smoking in children and adolescents. Targeted screening of these risk factors could be useful but specific screening strategies have to be evaluated. Research is needed to identify target populations, screening frequency, intervention, and follow-up. Meanwhile, efforts should rather focus on the primordial prevention of CVD risk factors and at maintaining a lifelong ideal cardiovascular health through environmental, policy, and educational approaches.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".