Assessment of Intakes and Patterns of Cooked Oatmeal Consumption in the U.S. Using Data from the National Health and Nutrition Examination Surveys
Bibliographic record
Abstract
The objective of the present study was to characterize the consumption of cooked oatmeal in the United States (U.S.) and to determine whether oatmeal consumption is associated with body mass index (BMI). To estimate current intakes of cooked oatmeal in the various age and gender population groups, we used dietary intake data from Day 1 of the U.S. 2009-2010 and 2011-2012 National Health and Nutrition Examination Surveys (NHANES). We also used dietary intake data from Day 1 of the U.S. 2003-2012 NHANES to assess associations between intakes of cooked oatmeal (in g/kg body weight) and NHANES cycle (2003-2004, 2005-2006, 2007-2008, 2009-2010, 2011-2012), age category (3-11 years, 12-18 years, 19-44 years, 45 years+), gender, and BMI classification (underweight, normal weight, overweight, or obese), using a multiple linear regression model. A consumer of oatmeal was defined as any individual who reported the consumption of any amount of oatmeal on Day 1 of the survey. Approximately 6% of the total population consumed oatmeal, with an average intake of 238 g/day of cooked oatmeal among consumers. The greatest prevalence of oatmeal consumption was in infants (14.3%) and older female adults (11.1%). Amongst oatmeal consumers, underweight, normal weight, and overweight individuals consumed significantly more oatmeal than obese individuals. Oatmeal was consumed almost exclusively at breakfast and, among consumers, contributed an average of 54.3% of the energy consumed at breakfast across all age groups examined. The association between oatmeal consumption and BMI is interesting and requires confirmation in future clinical studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| 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".