Bidirectional prediction between weight status and executive function in children and adolescents: A systematic review and meta‐analysis of longitudinal studies
Bibliographic record
Abstract
Summary This study examined the predictability of child weight status on executive function (EF) and vice versa. We searched PubMed, CINAHL, Web of Science, and EMBASE for longitudinal studies conducted in children and adolescents on October 31, 2021. A pairwise meta‐analysis was performed using a frequentist random‐effects approach. The quality of all included studies was evaluated using Newcastle–Ottawa Scale and GRADE assessments. This study included 18 longitudinal studies ( N = 30,101). Overall executive functioning was a significant negative predictor of child weight status (pooled beta coefficient = −0.14; 95% confidence interval [CI] [−0.22 to −0.07]; I 2 = 97%). The pooled odds ratio also revealed that high EF children had a significant lower risk for developing overweight/obesity (odds ratio [OR] = 0.72; 95% CI [0.59 to 0.87]; I 2 = 72%). Conversely, child weight status was a significant negative predictor of overall executive functioning (pooled beta coefficient = −0.06; 95% CI [−0.12 to −0.01]; I 2 = 81%). These results suggest a bidirectional prediction between child weight status and EF. These predictabilities are low but potentially beneficial for implementation in childcare systems.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".