Association between processed and ultra-processed food intake and the risk of breast cancer: a case-control study
Bibliographic record
Abstract
BACKGROUND: Results from studies investigating the association between ultra-processed foods (UPFs) and breast cancer are scarce and, in some cases, contradictory. Therefore, we aimed to evaluate the association between the intake of processed foods (PFs) and UPFs with the risk of breast cancer in Iranian women. METHODS: The present case (n = 133) - control (n = 266) study was carried out at two general hospitals in Tehran, Iran. A 168-item semi-quantitative food frequency questionnaire was used to assess the participants' dietary intake. Also, the NOVA classification was used to identify PFs and UPFs. The association between PFs and UPFs with the odds of breast cancer was analyzed using logistic regression models. RESULTS: According to Model 1 of conditional logistic regression, the odds of breast cancer were higher in the last tertile of UPFs than in the first tertile (odds ratio (OR) = 1.930; 95% confidence interval (CI): 1.080-3.449). In Model 2, no significant association was observed between the second and last tertiles of PFs and UPFs with the odds of breast cancer compared to the reference tertile. Also based on menopause status, the odds of breast cancer increased in the last tertile only among premenopausal women in Model 2 (OR = 3.656; 95% CI: 1.326-10.079). CONCLUSIONS: This study demonstrated that higher consumption of UPFs is associated with higher odds of breast cancer in premenopausal women.
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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 | 0.001 | 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 | 0.000 |
| 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".