Prescriptions of Traditional Chinese Medicine Are Specific to Cancer Types and Adjustable to Temperature Changes
Why this work is in the frame
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Bibliographic record
Abstract
Targeted cancer therapies, with specific molecular targets, ameliorate the side effect issue of radiation and chemotherapy and also point to the development of personalized medicine. Combination of drugs targeting multiple pathways of carcinogenesis is potentially more fruitful. Traditional Chinese medicine (TCM) has been tailoring herbal mixtures for individualized healthcare for two thousand years. A systematic study of the patterns of TCM formulas and herbs prescribed to cancers is valuable. We analysed a total of 187,230 TCM prescriptions to 30 types of cancer in Taiwan in 2007, a year's worth of collection from the National Health Insurance reimbursement database (Taiwan). We found that a TCM cancer prescription consists on average of two formulas and four herbs. We show that the percentage weights of TCM formulas and herbs in a TCM prescription follow Zipf's law with an exponent around 0.6. TCM prescriptions to benign neoplasms have a larger Zipf's exponent than those to malignant cancers. Furthermore, we show that TCM prescriptions, via weighted combination of formulas and herbs, are specific to not only the malignancy of neoplasms but also the sites of origins of malignant cancers. From the effects of formulas and natures of herbs that were heavily prescribed to cancers, that cancers are a 'warm and stagnant' syndrome in TCM can be proposed, suggesting anti-inflammatory regimens for better prevention and treatment of cancers. We show that TCM incorporated relevant formulas to the prescriptions to cancer patients with a secondary morbidity. We compared TCM prescriptions made in different seasons and identified temperatures as the environmental factor that correlates with changes in TCM prescriptions in Taiwan. Lung cancer patients were among the patients whose prescriptions were adjusted when temperatures drop. The findings of our study provide insight to TCM cancer treatment, helping dialogue between modern western medicine and TCM for better cancer care.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it