Foetal nutritional status and cardiovascular risk profile among children
Bibliographic record
Abstract
OBJECTIVE: To estimate the impact of foetal nutritional status on cardiovascular risk among children with the Foetal Nutritional Status Index (FNSI), calculated by dividing the child's birth weight (BW, kg) by the mother's height (m2). DESIGN: Cross-sectional survey analysis. SETTING: A sample of children from the US Third National Health and Nutrition Examination Survey. SUBJECTS: A total of 3109 children who were 5-11 years of age and had data on BW and mother's height. Non-fasting blood samples were included. RESULTS: Overall, the FNSI was positively associated with BW and negatively associated with mother's height (P<0.0001). Within sex-specific quintiles of FNSI (third quintile as reference) adjusted for potential confounding variables, cardiovascular risk factors tended to be 'higher' in the lower quintiles for males while the opposite was true for females. Multivariate logistic regression analyses indicated that the odds for males in quintile 1 was 2.4 for having a low level of high-density lipoprotein cholesterol (P<0.01) and 2.1 for having a cluster of cardiovascular risk factors (P=0.01); for females, the odds of having a cluster of cardiovascular risk factors was approximately two times higher for those in the first and fifth quintiles, who also had a significantly higher prevalence of central obesity. CONCLUSIONS: The FNSI may be a potential proxy indicator of foetal nutritional status and it may be used to test specific hypotheses of whether foetal nutrition restriction or overnutrition programmes future cardiovascular risk.
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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 | 0.000 | 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".