Childhood Overweight Obesity And Associated Factors Among Urban Children In Auranagabad A Cross Sectional Study.
Bibliographic record
Abstract
Background- Obesity in increasing global threat for many diseases and disorders. Objectives- Objective of this study were to identify the associated risk factors in the study population and to assess the relationship with the risk factors. Methods- This was the cross-sectional survey conducted using random sampling tech.in Aurangabad city. A total of 200 obese children studied. Dietary habits and lack of exercise, lifestyle factors was determined using questionnaire methods, BMI, MUAC tape. After successful evaluation of subjects, some dietary changes, lifestyle changes, regular 45 minutes of exercise is suggested and taken. results were taken after 15 days of regular session. Results – In this present study it was noted that the 58% were male and 41% were female almost 97% children’s were come to school by vehicle. The frequency of junk food is higher than vegetables, fruits. This leads to obesity in children. In our study focus on the on lifestyle intervention plan for children’s it was shown that the positive effect on improved participants dietary pattern and was effective in reducing excess weight programme was conducted for 30 days including with physical activity and nutritional counselling on obese adolescents and found a reduction in body weight. Conclusion - It concludes a higher prevalence of obesity among study subjects. It is implemented that decreased fast foods, having regular exercise, eating fiber, complex carbs, and essential fats are important to prevent obesity for better performance in study, for stronger immunity and better life style.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".