Effects of Curcumin on Treatment Outcome in Patients with Cancer Diagnosis
Bibliographic record
Abstract
Introduction: Treatment options for palliative care in patients with cancer aim to improve quality of life, and, in this context, alternative, complementary treatments are under study to reduce treatment side effects and increase traditional treatment efficacy. Curcumin is a food supplement derived from the plant Curcuma longa, which has recently received increasing attention because of its antioxidant and anti-inflammatory effects. Previous clinical trials, with different results, investigated Curcumin’s efficacy in cancer treatment. We aimed to explore the effect of Curcumin on treatment outcomes in patients with cancer diagnosis. Methods: In this systematic mini-review, conducted to answer the research question "What is the effect of curcumin on treatment outcome of cancer patients?" we searched four portals/databases (Pubmed/Medline, BVS/Lilacs, Scielo, and Cochrane). The PICOT strategy adopted was: P - patients with cancer; I - Curcumin; C- not applicable; O - treatment outcome; T- RCT and cohort studies. Independent reviewers checked for eligibility and study quality. Results: We included six studies regarding prostate cancer, head and neck tumors, colorectal cancer, breast cancer, and bladder cancer. Studies showed good tolerability for Curcumin with mild adverse effects. However, it showed no significant difference in survival or tumor progression. On the contrary, researchers observed exciting findings concerning preventing and relieving chemotherapy-related adverse effects. Discussion: Curcumin appears to be an intriguing potential adjuvant therapy in patients with cancer. Further studies on the topic are needed to investigate its possible concrete applications and to address the known problem of its poor bioavailability.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".