Issues of Type 2 Diabetes Disease Effective Treatment in Kazakhstan
Bibliographic record
Abstract
In his address to the people, the First President of our country, emphasized the need to introduce innovative methods of treating socially significant diseases. Among these diseases, diabetes holds a special position. More than 14,000 new cases of diabetes mellitus are officially detected annually in Kazakhstan.The real picture of the disease is difficult to compare with these data. This review discusses the prevalence of type 2 diabetes among the population of the Republic of Kazakhstan, and the causing factors such as age, race, genetic predisposition (OR = 3), obesity, glucose level and total cholesterol etc.It was found that the main complications and concomitant diseases of diabetes in residents of different regions are polyneuropathy - 22.4%, diabetic retinopathy - 14%, diabetic foot syndrome - 13.6%, arterial hypertension - 13.6% and coronary heart disease (CHD) - 14.4%. Only 1.8% of the population is diagnosed with type 2 diabetes, latent manifestations of type 2 diabetes mellitus, one in four people in Kazakhstan can be sick, 38% of adults aged 20-79 suffer from prediabetes, and 8.2% with diabetes. It is believed that by 2030 in Kazakhstan, there may be about a million patients with diabetes.Diabetes mellitus, in accordance with the Code of the Republic of Kazakhstan “On the health of the people and the health care system” belongs to the category of socially significant diseases.Therefore, the study of type 2 diabetes is one of the urgent problems of the public health in Kazakhstan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".