The Research Progress on China Major Minority Detoxification Methods
Bibliographic record
Abstract
Based on the research and material-sorting in recent decades involving Tibetan medicine Mongolia medicine Dai medicine Zhuang medicine Yao medicine, Yi medicine, Miao medicine, Jinuo medicine.Tibetan medicine has a unique system of medical theory, which posits that poisoning incidents have close relation with rLung, nKhris-pa, Badkan three factors (namely three due to), which by adjusting three due to is balance and achieve detoxification.Mongolian medicine believes that there are five-element doctrine, cold and heat, the strength and size of detoxifying respectively."Disease first solution, the solution after the first treatment," Dai medicine "Yajie" theory and the series of Yajie (antidote)", which play a role in detoxification lies.Zhuang medicine theory includes "virtual drug-induced diseases," the etiology and pathogenesis.By correcting the bias by that bias, in order to achieve the purpose of detoxification.Yao medical theory: the Profit and Loss Balance Theory, the Cause Theory, which are related to detoxification.According to the theory of three gas, toxin factor theory as the core contents of Yi medical theory, which the application of detoxification method is associated with.Miao medicine thinks Poison for all ills, highlight the dispel toxin factor to the poison thery of nine.Jinuo medicine is fully application of national folk-detoxification plant and animal medicines, achieve the purposes of detoxification.In this review, we summarized the major approaches applied for detoxification methods by different ethnic groups in order to provide better guide for clinical practice of food, drugs, poisons and other toxics.This work provides also potential clinical application to open up a new way of thinking and new perspective for detoxification therapy based on the accumulated knowledge in traditional Chinese medicine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".