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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".