Associations of dietary dark-green and deep-yellow vegetables and fruits with cervical intraepithelial neoplasia: modification by smoking
Bibliographic record
Abstract
Smoking has been positively and fruit and vegetable intake has been negatively associated with cervical cancer, the second most common cancer among women worldwide. However, a lower consumption of fruits and reduced serum carotenoids have been observed among smokers. It is not known whether the smoking effect on the risk of cervical neoplasia is modified by a low intake of fruits and vegetables. The present study examined the combined effects of tobacco smoking and diet using a validated FFQ and serum carotenoid and tocopherol levels on cervical intraepithelial neoplasia grade 3 (CIN3) risk in a hospital-based case-control study conducted in São Paulo, Brazil, between 2003 and 2005. The sample comprised 231 incident, histologically confirmed cases of CIN3 and 453 controls. A low intake ( ≤ 39 g) of dark-green and deep-yellow vegetables and fruits without tobacco smoking had a lesser effect on CIN3 (OR 1·14; 95 % CI 0·49, 2·65) than among smokers with higher intake ( ≥ 40 g; OR 1·83; 95 % CI 0·73, 4·62) after adjusting for confounders. The OR for the joint exposure of tobacco smoking and low intake of vegetables and fruits was greater (3·86; 95 % CI 1·74, 8·57; P for trend < 0·001) compared with non-smokers with higher intake after adjusting for confounding variables and human papillomavirus status. Similar results were observed for total fruit, serum total carotene (including β-, α- and γ-carotene) and tocopherols. These findings suggest that the effect of nutritional factors on CIN3 is modified by smoking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".