The Comparison between Chinese Traditional medicine and Low Carbohydrate Diet on the treatment of Type 2 diabetes, a randomized Controlled Trail
Bibliographic record
Abstract
Diabetes is a crucial problem that the world is facing, especially type 2 diabetes because of the large number of patients and the death brought by it. This paper compares specifically two treatments of type 2 diabetes – low carbohydrate diet (LCD) and Chinese traditional medicine (CTM), through an RCT. Participants are randomly assigned to three groups – low carbohydrate diet group, Chinese traditional medicine group and the control group. The conclusion got from the comparisons between the data of three groups shows that both LCD and CTM are effective in treating type 2 diabetes since they show statistically significant decreases in blood glucose levels, A1c levels, and waist circumferences, and there is no statistically significant difference between their effectiveness. This work proves the validity of the two treatments and provides the foundation for further research on the treatments of both type 1 and 2 diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".