A comprehensive meta‐analysis on dietary flavonoid and lignan intake and cancer risk: Level of evidence and limitations
Bibliographic record
Abstract
SCOPE: To summarize available evidence on the association between dietary flavonoid as well as lignan intake and cancer risk in observational studies. METHODS AND RESULTS: A systematic search on electronic databases of all English language case-control and prospective studies published up to June 2016 was performed. Risk ratios (RRs) and 95% confidence intervals were calculated by random-effects model separately by study design. Heterogeneity and publication bias were tested. Out of the 143 studies included, meta-analyses of prospective studies showed isoflavones significantly associated with decreased risk of lung and stomach cancers and nearly significant breast and colorectal cancers; total flavonoids showed nonsignificant decreased risk of breast cancer. Meta-analyses of case-control studies showed: total and/or individual classes of flavonoids associated with upper aero-digestive tract, colorectal, breast, and lung cancers; isoflavones with ovarian, breast, and colorectal cancers, endometrial and lung cancers. CONCLUSIONS: Most evidence reported in previous meta-analyses was driven by case-control studies. Overall results may be promising but are inconclusive. Further prospective cohorts assessing dietary polyphenol exposure and studies using other methods to evaluate exposure (i.e. markers of consumption, metabolism, excretion) are needed to confirm and determine consumption levels required to achieve health benefits.
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 | 0.024 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".