Bibliographic record
Abstract
Oncology, from a traditional Chinese medicine (TCM) perspective, is known as Ai Zhi, which signifies physiological accumulation of the mass, which is pushing out and spreading to various cells and organs in the body. It is also associated with the accumulation of phlegm, which may be generally defined as a pathogenic, chronic accumulation of mucus in various organ systems, especially the stomach and the bladder organ/meridian systems, which causes severe blockage of Qi (vital energy), subsequently causing blood stagnation and mass formation. This may lead to cancer. Another major cause of cancer, according to TCM theory, is blood stasis. In TCM oncology, the major emphasis is to eliminate phlegm and blood stagnation/stasis. Various methods are used for these purposes, and nutritional/herbal medicine plays a central role. Mushrooms were proven clinically useful in fighting cancer, especially the Ganoderma lucidum (W.Curt.:Fr.)Lloyd (Ling Zhi or Reishi), Lentinus edodes (Berk.) Singer (Shiitake) species.Mushrooms are good not only for immunoenhancement, but also to complement Western chemotherapy and radiation therapy. Mushrooms contain a number of polysaccharides and secondary metabolites, which work by countering the side-effects of cancer such as nausea, bone marrow suppression, anemia, and lowered resistance.It is also vitally important to consider the aspects of internal body harmony in conjunction with the mental and spiritual aspects, involving the suffering patients, their families and friends, and the various physicians and other caregivers involved. In medical acupuncture clinics, certain medicinal mushroom products approved by the Canadian government and other governments are useful adjuncts in treating invasive tumors and the pain of cancer as well as the side-effects of chemotherapy, radiation, and surgery, such as the following:
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".