Bibliographic record
Abstract
The use of complementary and alternative medicine is gaining popularity worldwide. Cancer patients are major consumers of natural health products for a variety of reasons; the most common is to build the body’s defense by augmenting the immune system. Various species of mushrooms have been studied for decades because of their alleged immuno-stimulating properties. Active substances from more than fifty species of mushrooms have been isolated and found to have such properties. Of these, polysaccharide-K and polysaccharide-peptide, extracted from the mushroom Coriolus versicolor (CV), have been more systematically investigated in human cancer research. CV extracts are extremely popular in certain ethnic communities with a long tradition of employing healing practices that are unconventional in nature compared with western medicine approaches. Cancer patients are using CV extracts as part of their adjunctive cancer therapy. Cancer specialists and allied health care professionals may not be fully aware of such a choice especially when patients do not disclose the information. As communities in North America are becoming more culturally diverse, the rise in the use of natural health products is most likely inevitable. Better understanding of some popular natural health products and the reasons behind their use may foster communication between patient and health care provider and is crucial in helping patients address their needs and concerns in the hope of optimizing cancer care. Exploring the mechanism of CV extracts, their safety, risks and possible benefits is one step toward these goals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".