Association Between Consumption of Fruits and Vegetables and Risk of Colorectal Adenoma
Bibliographic record
Abstract
There have been contradictory results about the association of fruits and vegetables intake with colorectal adenoma (CRA) risk, the precursor lesion of colorectal cancer. Herein, we have conducted a meta-analysis of the published observational studies to have a clear understanding about this association.Eligible studies up to November 30, 2014, were identified and retrieved by searching MEDLINE and EMBASE databases along with the manual review of the reference list of the retrieved studies. The quality of the included studies was evaluated using Newcastle-Ottawa Quality Assessment Scale, and random-effects model was used to calculate summary relative risk (SRR) and corresponding 95% confidence interval (CI).A total of 22 studies involving 11,696 CRA subjects were part of this meta-analysis. The SRR for the highest versus the lowest intake of vegetables alone was 0.91 (95% CI: 0.80-1.02, Pheterogeneity = 0.025), whereas for vegetables and fruits combined, it was 0.82 (95% CI: 0.75-0.91, Pheterogeneity = 0.369), and for fruits alone, it was 0.79 (95% CI: 0.71-0.88, Pheterogeneity = 0.111). In addition, linear dose-response analysis also showed similar results, for example, for per 100 g/d increment of fruits, the SRR was 0.94 (95% CI: 0.92-0.97) and for vegetables it was 0.98 (95% CI: 0.96-1.01). Nonlinear association was only observed for vegetables (Pnonlinearity = 0.024), but not for fruits (Pnonlinearity = 0.583).Thus, this meta-analysis suggested that fruits consumption have a significant protective effect on CRA risk, but not vegetables. Moreover, we recommend additional studies with prospective designs that use validated questionnaires and control for important confounders to further validate the overall results.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".