Prevalence of Underweight and Overweight and Its Association with Physical Fitness in Egyptian Schoolchildren
Bibliographic record
Abstract
Underweight and overweight are serious health concerns for many children and could be associated with low physical-fitness levels. This study aimed (i) to evaluate the prevalence of underweight and overweight and (ii) to examine its association with the physical fitness levels in primary male and female schoolchildren. Including 13 government primary-schools, a cross-sectional survey was conducted between 2014 and 2017. Anthropometric characteristics together with the physical-fitness level were measured in 931 schoolchildren aged between 6- and 11-years old. The prevalence of under- and overweight children were 8.49% and 24.06%, respectively. These proportions were not significantly different between males and females and were affected by age (p < 0.001), with a higher prevalence of overweight and a lower prevalence of underweight at 9–11 years, compared to 6–8 years old. Concerning the physical fitness levels, statistical analysis showed a better performance among males compared to females, among participants aged 9–11 years, compared to 6–8 years old, and among underweight and normal-weight, compared to overweight children (p < 0.001). There was a higher prevalence of overweight and lower prevalence of underweight at 9–11 years compared to 6–8 years old. Physical fitness levels were better in (i) males, compared to females, (ii) schoolchildren aged 9–11 years, compared to 6–8 years old, and (iii) underweight and normal-weight, compared to overweight children.
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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 | 0.001 | 0.001 |
| 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".