Gaussian Model to Predict the Risk of Developing Type 2 Diabetes Mellitus in Mexican Population Taking as a Reference Risk Factors
Bibliographic record
Abstract
In this research work, an ordinal Gaussian model is constructed, whose objective is to predict the degree of risk of contracting type 2 diabetes mellitus (2DM), taking as reference the risk factors in the Mexican population. It is estimated that the Mexican population has a hereditary susceptibility to develop 2DM, however, the probability increases depending on risk factors; area of residence, background of parents with 2DM, tobacco consumption, alcohol consumption, physical inactivity, body mass index (BMI), and type of feeding, which, despite positively intervening in the appearance of 2DM, they can be modified to obtain the inversely proportional effect. However, the social, economic and political context are important components for the population. Risk factors, as explanatory elements of the prevalence of 2DM, are of the utmost importance to delay or control their early development, as some are factors that can be muffled. For the development of this model, the information published in the National Health and Nutrition Survey (ENSANUT) of 2012 was taken, based on the adult population 20 years of age or older. Among the most outstanding results is the higher prevalence of risk that women have with respect to men, and the fact that age is a fundamental basis for contracting type 2 diabetes mellitus.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".