Bibliographic record
Abstract
Background: The prevalence of diabetes mellitus in Indonesia in 2013 by 2.1% increased from 2007 which wasonly 1.1% and DKI Jakarta was above the national average prevalence of 3.0%.Objectives: Assess the influence of lifestyle (physical activity, diet, smoking behavior, and obesity) on theincidence of type 2 diabetes mellitus.Method: This is a quantitative study with case control design. The case population in this study was all patientswith type 2 diabetes mellitus and the controls were all parien who did not have diabetes mellitus. The size ofthe sample is calculated by using a large rusmus hypothesis test sample of 2 proportions. The minimum samplecount for cases was 162 cases and 162 controls, the total sample was 324 respondents. The statistical test usedis the Chi Square test.Results: The results showed that the proportion of respondents aged ? 40 years (68.8%), female (49.4%), poorlyeducated (23.5%), there was a history of diabetes mellitus (41.4%), less physical activity (59.0%), unbalanceddiet (53.7%), smoking (31.2%) and obesity (41.0%). The results of bivariate analysis showed that the variablesthat affect the incidence of type 2 diabetes mellitus were physical activity (p=0000, OR=4,914), diet (p=0.001,OR=2.125), and obesity (p=0000, OR=2,622). While smoking behavior has no effect on the incidence of type 2diabetes mellitus.Conclusion: Lifestyles that affect the incidence of Type 2 DM are lack of physical activity, unbalanced diet, andobesity. Smoking cannot be proven.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".