Screening for risk factors for type 2 diabetes mellitus using the Canadian Diabetes Risk Questionnaire (CANRISK) in the East Java Provincial Health Service, Indonesia
Bibliographic record
Abstract
Diabetes Mellitus remains a serious and growing global challenge for public health. Early identification of cases prevents delays in treating diabetes mellitus, which often causes various complications in the body. Diabetes Mellitus screening includes anamnesis for family and personal history of the disease, measurement of height, weight, abdominal circumference, blood pressure examination, and examination of sugar levels. One instrument that can be used to assess the risk of developing type 2 diabetes mellitus is the Canadian Diabetes Risk Questionnaire (CANRISK). The aim of this research is to find out the characteristics of respondents and the risk categories of respondents suffering from type 2 diabetes mellitus in the next 10 years using CANRISK. This research uses quantitative methods. Based on the type of research, this research uses descriptive observational research. The research design used was cross sectional. The results showed that 78% of respondents had a low-moderate risk of developing type 2 DM, 6% had a high risk of developing type 2 DM, and 16% had a very high risk of developing type 2 DM. The conclusion of this study was that the risk factors for developing Type 2 DM 2 are age, BMI, waist circumference, physical activity habits, vegetable and fruit consumption habits, history of high blood pressure and high blood sugar, history of giving birth to a baby more than 4.1 kg, family history of diabetes, parents' ethnic group, and level of education.
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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) | 0.001 | 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".