Mealtime media use and cardiometabolic risk in children
Bibliographic record
Abstract
OBJECTIVES: To examine the association between mealtime media use and non-HDL-cholesterol as well as other markers of cardiometabolic risk (CMR) in children. DESIGN: A repeated measures study design was used to examine the association between mealtime media use and CMR outcomes. Multivariable linear regression with generalised estimating equations was used to examine the association between mealtime media use and CMR outcomes. Analyses were stratified a priori by age groups (1-4 and 5-13 years). SETTING: The TARGet Kids! Practice-based research network in Toronto, Canada. PARTICIPANTS: 2117 children aged 1-13 years were included in the analysis. RESULTS: After adjusting for covariates, there was no evidence that total mealtime media use was associated with non-HDL-cholesterol in 1-4 year olds (P = 0·10) or 5-13 year olds (P = 0·29). Each additional meal with media per week was associated with decreased HDL-cholesterol in 5-13 year olds (-0·006 mmol/l; 95 % CI -0·009, -0·002; P = 0·003) and log-TAG in 1-4 year olds (β = -0·004; 95 % CI -0·008, -0·00009; P = 0·04). Media use during breakfast was associated with decreased HDL-cholesterol in 5-13 year olds (-0·012 mmol/l; 95 % CI -0·02, -0·004; P = 0·002), while media during lunch was associated with decreased log-TAG (-0·01 mmol/l; 95 % CI -0·03, -0·002; P = 0·03) in children aged 1-4 years. Total mealtime media use was not associated with total cholesterol, glucose or insulin in either age group. CONCLUSIONS: Mealtime media use may be associated with unfavourable lipid profiles through effects on HDL-cholesterol in school-aged children but likely not in pre-schoolers.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".