Dietary patterns and breast cancer risk among women
Bibliographic record
Abstract
OBJECTIVE: Breast cancer is the most common type of cancer in women worldwide. Several studies have examined the role of single nutrients and food groups in breast cancer pathogenesis but fewer investigations have addressed the role of dietary patterns. Our main objective was to identify the relationship between major dietary patterns and breast cancer risk among Iranian women. DESIGN: Hospital-based case-control study. SETTING: Shohada Teaching Hospital, Tehran, Iran. SUBJECTS: Overall, 100 female patients aged 30-65 years with breast cancer and 174 female hospital controls were included in the present study. Dietary intake was assessed using a valid and reliable semi-quantitative FFQ consisting of 168 food items. RESULTS: Two dietary patterns were identified explaining 24·31 % of dietary variation in the study population. The 'healthy' food pattern was characterized by the consumption of vegetables, fruits, low-fat dairy products, legumes, olive and vegetable oils, fish, condiments, organ meat, poultry, pickles, soya and whole grains; while the 'unhealthy' food pattern was characterized by the consumption of soft drinks, sugars, tea and coffee, French fries and potato chips, salt, sweets and desserts, hydrogenated fats, nuts, industrial juice, refined grains, and red and processed meat. Compared with the lowest tertile, women in the highest tertile of the 'healthy' dietary pattern score had 75 % decreased risk of breast cancer (OR = 0·25, 95 % CI 0·08, 0·78), whereas women in the highest tertile of the 'unhealthy' dietary pattern had a significantly increased breast cancer risk (OR = 7·78, 95 % CI 2·31, 26·22). CONCLUSIONS: A healthy dietary pattern may be negatively associated with breast cancer risk, while an unhealthy dietary pattern is likely to increase the risk among Iranian women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".