Dietary Flavonoids and Lung Cancer: A GRADE-Assessed Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
Individual observational studies examining the association between polyphenols and the risk of lung cancer have reported mixed findings. Therefore, we performed a systematic review and meta-analysis to determine the pooled effects between polyphenol intake and lung cancer risk. A systematic search was performed on PubMed, Scopus, and Web of Science databases in April 2023. Random-effect models were used to estimate odd ratios (OR) and 95% confidence intervals (95% CI). In total, 20 studies were included in the systematic review. The pooled analyses indicated that a higher intake of flavonoids (OR = 0.81; 95% CI: 0.67,0.98; p = 0.03) and isoflavone (OR = 0.82; 95% CI: 0.74,0.92; p < 0.001) were associated with lower odds of lung cancer. In addition, the ingestion of anthocyanidin (OR = 0.80; 95% CI: 0.65,0.98; p = 0.04), kaempferol (OR = 0.78; 95% CI: 0.64,0.96; p = 0.02), quercetin (OR = 0.66; 95% CI: 0.48,0.91; p = 0.01) and flavanones (OR = 0.71; 95% CI: 0.59,0.85; p < 0.001) reduced the likelihood of developing lung cancer. Overall, our findings suggest that flavonoids, isoflavones, anthocyanidin, kaempferol, quercetin, and flavanones may protect against lung cancer.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".