Curcumin and cancer: barriers to obtaining a health claim
Bibliographic record
Abstract
Curcumin is a highly pleiotropic molecule found in the rhizomes of Curcuma longa (turmeric). It is responsible for the yellow color of turmeric and has been shown to inhibit the proliferation of cancer cells and to be of use in preventing or treating a number of diseases. Curcumin has been shown to modulate multiple cell-signaling pathways simultaneously, thereby mitigating or preventing many different types of cancers, including multiple myeloma and colorectal, pancreatic, breast, prostate, lung, head, and neck cancers, in both animal models and humans. Current therapeutic approaches using a single cancer drug for a single target can be expensive, have serious side effects, or both. Consequently, new approaches to the treatment and prevention of cancer, including the integration of curcumin as a viable treatment strategy where dysregulation of many pathways is involved, are warranted. A methodical review of the evidence was performed to evaluate the effects of curcumin in support of a health claim, as established through the regulatory framework of Health Canada, for a relationship between the consumption of curcumin and the prevention and treatment of cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".