Comparison of Traditional Chinese Medicine and Traditional Iranian Medicine in Diagnostic Aspect
Bibliographic record
Abstract
Iranian traditional medicine (TIM) has a long and old history from ancient periods up to now and it is used in prevention, diagnosis, treatment, and elimination of diseases in Persia and neighboring countries. In Traditional Iranian Medicine, physiological functions of the human body are based on 7 factors: Elements, Temperament, Humors, Organs, Spirits, Forces or Faculty, Functions. Traditional Chinese Medicine (TCM) with 3000-5000 year of history has a unique system to diagnosis and prevention of diseases. TCM with acupuncture and Chinese herbal medicine is one of the most important parts in complementary and alternative medicine. The clinical diagnosis and treatment in TCM are mainly based on the yin-yang and five elements theories. The aim of present study is to assess differences of TCM and TIM in diagnostic aspect for this purpose we searched Iranian databases and 30 years review articles of the Chinese scholar database (CNKI, VIP…) and relevant articles published in Journals inside and outside of China without language restrictions. The results showed that diagnosis in TIM is mostly focused on urine analysis, smelling, and pulse-taking, while a diagnosis of diseases in TCM is mainly focused on tongue observation and pulse taking. It seems that through the time some parts of diagnosis are missed. If practitioners take advantages from traditional medicine and combine it with the science of western medicine, it could be a great help for integrative medicine. Our knowledge about each of the traditional medicine not only should not be against the other types of traditional medicine but also it should be a help for finding information about missed parts.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".