Prevalence of obesity using new adiposity indices among the Barwar community: A Denotified Tribe of Gonda district, Uttar Pradesh, India
Bibliographic record
Abstract
Introduction The present study assesses the prevalence of obesity using new adiposity indices and evaluatesthe association between socio-demographic factors and combined obesity among the denotified tribe of Gonda district, Uttar Pradesh, India. Materials and methods The present study was undertaken among 315 rural adults (141 males; 174 females) aged 20–59 years. Sex wise mean differences were observed by the t test. The Chi-square tests were used for any significant relationships between categorical variables. Binary logistic regression was used to identify the risk factors for combined obesity. ROC curve was used for predicting the combined obesity. Results The prevalence's of combined general obesity, combined central obesity, and overall combined obesity are 34.9%, 69.8%, and 75.6% respectively.Combined obesity always shows higher prevalence of obesity than any obesity-related single parameter. The prevalence of single obesity, dual obesity, and no obesity is 46.3%, 29.3% and 24.4% respectively. In the case of all socio demographic parameters, widow shows the high risk of CGO (OR = 8.83, CI: 1.97–39.58, p=<0.01) and the higher age groups (50–59 years) show the higher risk of CCO (OR = 4.95, CI: 2.47–9.90, p=<0.001) and OCO (OR = 6.51, CI: 2.68–15.84, p=<0.001). Conclusion The current study concludes that any single parameter or index is not enough to measure the actual prevalence of obesity. The present study shows females have a higher prevalence of all three types of combined obesity than males. Combined obesity assesses the overall magnitude of obesity and identifies communities with multiple obesity parameters.
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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 | 0.001 | 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".