H.M. The King’s Initiation on Metabolic Syndrome among Adult in Yao Noi Island, Thailand
Bibliographic record
Abstract
The cross-sectional study aimed to examine the prevalence of metabolic syndrome among adults in Yao Noi Island, Phangnga Province, Thailand. The survey was conducted with randomly selected areas including 7 villages from Yao Noi Island, Phangnga province, Thailand. Two hundred and twenty-seven adults with aged more than 20 years old and concluded the study between October and December, 2010. The definition of Metabolic syndrome is defined by the modified National Cholesterol Education Program Adult Treatment Panel III report. The questionnaire, physical and clinical examination were collected. The prevalence of metabolic syndrome was 12.8% in average, with women (13.9%) more than men (9.1%) respectively. The most common risk factors of metabolic syndrome in women were the abdominal obesity of more than 80 cm (61.6%), a high FPG (30.3%) and elevated TG (20.9%). Among men, the risk factors were the abdominal obesity of more than 90 cm (16.4%), a high FPG (47.3%) and high elevated TG (32.7%). The prevalence increased from 8.7% among subjects aged between 20 and 29 years to 21.2% with the subjects aged 50 years or older. These findings suggest that MS is becoming a remarkable health problem in Thailand. The preventive measurement can reduce mortality especially healthy life style education programs such as weight reduction activity, physical activity and healthy diets.
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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 | 0.001 | 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) | 0.004 | 0.001 |
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".