PREDICTION OF THE RISK OF DEVELOPING TYPE 2 DIABETES MELLITUS USING BODY MASS INDEX IN ADOLESCENTS
Bibliographic record
Abstract
BACKGROUNDOverweight and obesity are recognized as an escalating epidemic affecting both developed and developing countries.Body Mass Index (BMI) is promulgated by WHO as the most useful epidemiological measure of obesity.Obesity is one of the most important modifiable risk factors in pathogenesis of type 2 diabetes mellitus.Studies have indicated that Indians are highly susceptible to diabetes even with modest overweight, central obesity and physical inactivity.The objectives of this study were-1.to determine the relation between body mass index and fasting blood sugar 2. to determine the factors influencing childhood obesity and type 2 diabetes. MATERIALS AND METHODSAfter obtaining consent from the school authorities and parents, details of the children were collected.Weight, height and fasting blood sugar of the children were measured.Body Mass Index was calculated.The data was analysed statistically. RESULTSThe prevalence of overweight was 14.8% and obesity was 9.0%.There is a positive correlation between Body Mass Index and Fasting Blood Sugar with a r value of 0.826 and a p value of 0.0001.Socioeconomic status and TV watching time have a positive influence on Body Mass Index.Family history of type 2 Diabetes Mellitus has a positive correlation with Body Mass Index and Fasting Blood Sugar. CONCLUSIONThe prevalence of overweight and obesity among adolescent children is on a raising trend.There is a positive correlation between fasting blood sugar and Body Mass Index.Socioeconomic status and TV watching time have a positive influence on Body Mass Index.Family history of type 2 Diabetes Mellitus has a positive correlation with Body Mass Index and Fasting Blood Sugar.
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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 | 0.000 |
| 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) | 0.001 | 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".