Green Tea and Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Green tea is a beverage widely used by lung cancer patients and the public for its purported anticancer properties. The authors conducted a systematic review of green tea for the treatment and prevention of lung cancer. METHODOLOGY: Six electronic databases were searched from inception until November 2011 for human interventional and preclinical evidence pertaining to the safety and efficacy of green tea for lung cancer. RESULTS: A total of 84 articles met inclusion criteria: two Phase I trials, three reports of one surrogate study, and 79 preclinical studies. There is a lack of controlled trials investigating green tea for lung cancer. Two Phase I studies showed no objective tumor responses at the maximum tolerated dose, ranging from 3 to 4.2 g/m(2) green tea extract (GTE) per day. Four cups of green tea daily decreased DNA damage (8OH-dG) in smokers. Human studies indicate that 800mg of green tea catechins daily does not alter activity of the CYP2D6, CYP1A2, CYP3A4 and CYP2C9 enzymes, however in vitro evidence suggests that green tea may bind to and reduce the effectiveness of bortezomib. Green tea applied topically may improve the healing time of radiation burns. CONCLUSIONS: Although some evidence suggests that chemopreventative benefits can be accrued from green tea, there is currently insufficient evidence to support green tea as a treatment or preventative agent for lung cancer. Green tea should not be used by patients on bortezomib therapy. Further research is warranted to explore this natural agent for lung cancer treatment and prevention.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".