Contribution of Metabolic Syndrome in Controlling Diabetes Mellitus According to Gender in Indonesia (RISKESDAS 2018)
Bibliographic record
Abstract
BACKGROUND: Metabolic syndrome (MetS) is a multiple risk factor for the development of type 2 diabetes mellitus (DM). It is important to understand the contribution of MetS in developing DM in different population characteristics. This study aims to obtain the prevalence of MetS and the magnitude of the contribution of MetS risk factors as a basis for developing targeted DM intervention programs. METHODS: This study used data from the 2018 Riskesdas survey, an Indonesia national health survey, with a total sample of 24,545 individuals aged 15 years and over. This study selected only respondents who had never been diagnosed with diabetes mellitus before the survey was conducted and have complete MetS data according to the National Cholesterol Education Program or Adult Treatment Panel III (NCEP/ATP III) criteria. Data had been analyzed for the Population Attributable Fraction (PAF) statistical test. RESULTS: A total of 29.2 percents of the population with MetS and the prevalence in women (17.2%) was higher than in men (11.9%) Three components of MetS that contribute greatly to DM were fasting blood glucose levels, hypertension and high triglyceride levels. If the men population can maintain two risk factors (fasting blood sugar levels and blood pressure) under normal conditions, the prevalence of DM can be reduced by as much as 15 percent. In women, if three factors (fasting blood sugar levels, blood pressure, and triglyceride levels) can be maintained under normal conditions, the prevalence of DM can be reduced by 29.9 percent. CONCLUSION: Prevention strategy of DM need to include monitoring and controlling of the metabolic syndrome and behavioral risk factors, that can be applied in primary health center as well as in community-based setting of health program.
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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 | 0.000 | 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".