A Comparative Study of Body Mass Index, Body Weight and Waist to Height Ratio to Depict Serum Cholesterol Level in Healthy Young Individuals
Bibliographic record
Abstract
Introduction: Many external and internal factors either directly or indirectly regulate our health.Similarly, many parameters such as high glucose, high cholesterol, and high blood pressure are the indicators of our healthiness.Body Mass Index (BMI) and Waist to Height Ratio (WtHR) are such parameters which indicate the degree of healthiness of an individual.Current study aimed at estimation and comparison of the statistical relationships of BMI, body weight and WtHR with serum cholesterol level in healthy Individuals of age group of 18-30.Material and methods: This study was done on total 54 healthy persons (27 male and 27 females) of age group 18-30.The data of age, height weight and waist circumference of all participants were collected.In their fasting blood samples, total serum cholesterol was measured by colorimetric kit.In this study statistical correlation was confirmed by three different statistical methods.Results: Our statistical analysis suggested that BMI, body weight and WtHR are positively correlated with average total serum cholesterol level with a significant p value (<0.05).Statistical correlation coefficient values further suggested that BMI could be a better predictor of cholesterol level associated diseases as compared to body weight and WtHR in healthy individuals of age group 18-30.High BMI and Waist circumference are indicators of overweight and/ obesity.Conclusion: These findings indicated that BMI could be a better predictor of cholesterol level associated diseases as compared to body weight and WtHR in healthy individuals of age group 18-30.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".