Comparative Analysis of Diabetes in China and The United States-Based on Risky Factor, Complications and Quality of Life
Bibliographic record
Abstract
Of the two main types of diabetes, the most prevalent kind of diabetes is type 2 diabetes (T2D). In many of the world's countries with high diabetes rates and large populations, China and the United States are two nations that can be used as a point of reference when trying to find a solution to the diabetes problem. This paper analyzes the differences between China and the United States in terms of three aspects: risk factors, complications and the quality of life-related to this disease. Obesity is a risk factor that has a significant impact on diabetes in both the United States and in China, and smoking is also one of the risk factors which is more prominent in China. In terms of complications, the complications in the United States and China are almost the same, and the incidence is basically similar. The poor performance of pancreatic beta-cells and the disparity in treatment levels, however, are likely to blame for China's considerably greater prevalence of diabetic complications than the US. In China, the living quality of patients with any kind of diabetes depends more on the patient's economic status and education level, while in the United States, depends on cultural differences. Race is also an important factor affecting patients’ quality of life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".