Differing effects of statistical approaches to assess the relationship between egg consumption patterns and adiposity using data from 2001–2008 NHANES
Bibliographic record
Abstract
Associations between food patterns and adiposity are poorly understood. Three statistical approaches examining the association between egg consumption and adiposity were tested. Participants (n=18,987) were 19 + years from 2001–2008 NHANES. 24 hour diet recall data provided intake; body mass index (BMI) and waist circumference (WC) determined adiposity. Least‐square means ± SE, adjusting for appropriate covariates, were generated. The first statistical approach categorized participants into egg or non‐egg consumers. Consumers had higher mean BMI (p=0.006) and WC (p=0.002) than non‐consumers. Second, cluster analysis identified 8 distinct egg consumption patterns (explaining 34.5% of the variance in total energy intake). Two egg patterns (egg/meat, poultry, fish [MPF]/grain/vegetables & egg/MPF/grain), consumed by ≤2% of the population, drove the association (compared with no egg pattern) between egg consumption and BMI and WC. The third approach controlled for other food groups consumed with eggs in those two egg patterns. Only the egg/MPF/grain pattern remained associated with BMI and WC (both p≤0.0063). Care needs to be taken with data interpretation of diet and health risk factors and the choice of statistical analyses since these studies are used to generate hypotheses. Additional studies are needed to better understand these relationships. 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 | 0.554 | 0.635 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.017 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".