Empirical Analysis of Body Constitution and Food Intake in Persons with Type 2 Diabetes from a TCM Perspective
Bibliographic record
Abstract
This study examined the correlations between body constitution (BC) and food intake in a sample of persons with Type 2 Diabetes (T2DM) from a perspective of traditional Chinese medicine (TCM). Past research on BC of persons with diabetes (DM) from a TCM perspective revealed imbalanced state of Yin and Yang in terms of Yin-deficiency (YID), Yang-deficiency (YAD), and Yin-Yang-deficiency (YYD). However, no studies have attempted to find out if daily food intake has an influence on Yin–Yang balance. The present study adapted a mixed method, which constituted of two phases. Phase one involved an exploratory case study (n=18) conducted between May and June 2011 and phase two, a descriptive correlation study (n=210) between October and December 2013. Results showed that in phase-one, three cases showed YID and higher food intake in hot/warm nature, 12 cases with YAD and higher food intake in cold/cool nature while three cases with Yin-Yang-deficiency (YYD) and extremely high food intake in cold/cool nature. In phase-two, Spearman’s correlation coefficient between food intake and YID presentations (YIDPs) (hot/warm food: rho=0.34, p=0.000; cold/cool food: rho= 0.18, p=0.006); YAD presentations (YADPs) (hot/warm food: rho=0.18, p=0.008; cold/cool food: rho=0.2, p=0.006); and YYD presentations (YYDPs) (hot/warm food: rho=0.29, p=0.006; cold/cool food: rho=0.2, p=0.003) have been noted. The findings concluded that persons with T2DM and YIDPs, YADPs, or YYDPs tend to have food intakes higher in hot/warm nature or cold/cool nature
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".