Relationship between egg consumption patterns and nutrient intake, diet quality, weight measures, and cardiovascular risk factors (CVRF)‐2001–2008 NHANES
Bibliographic record
Abstract
This study examined the association of egg consumption patterns (ECP) with nutrient intake, diet quality, adiposity, and CVRF in adults participating in NHANES 2001–2008. 24‐hour dietary recalls and cluster analysis determined the ECP; BMI and waist circumference (WC) assessed adiposity; CVRF were serum lipids/blood pressure/triglycerides/glucose/insulin. Covariate adjusted LS means ± SE were generated. Eight ECP were created (34.5% of the variance in total energy intake). ECP were compared to the no egg pattern, which was the most common ECP (80.2% of the sample) followed by the egg/meat, poultry, fish [MPF]/grain/potato/fruit juice/vegetable pattern (7.8%). 1–3% of the sample consumed one of the six remaining ECP. Most ECP resulted in higher energy intake. Several ECP showed lower intakes of added sugars, but higher intakes of saturated and solid fats. Total nutrient intake varied in the ECP; three ECP had lower diet quality. Two ECP were associated with BMI and WC; when food groups consumed in those ECP were covariates, only one ECP remained significant (egg/MPF/grain; (p≤0.0063). Only one ECP was positively associated with diastolic blood pressure and LDL‐C (both p≤0.0063). Relationships between food patterns and health outcomes are complex. More studies are needed to determine what foods drive these associations and how foods consumed at other meals influence results. Support: USDA & Egg Nutrition Board.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".