Dietary patterns and risk of cancer of various sites in the Norwegian European Prospective Investigation into Cancer and Nutrition cohort: the Norwegian Women and Cancer study
Bibliographic record
Abstract
An indicator of common diets among groups of individuals can be found by identifying dietary patterns. We found previously six dietary patterns in the Norwegian European Prospective Investigation into Cancer and Nutrition cohort and labelled them fish, healthy, average, western, bread and alcohol. We examined the relationship between the different patterns and risk of total cancer, breast cancer and gastrointestinal cancers in 34 471 women from the Norwegian European Prospective Investigation into Cancer and Nutrition cohort, in which there were 1355 cancer cases. The hazard ratios and their corresponding 95% confidence intervals were estimated using Cox proportional hazards regression. Stratified analysis on menopausal status and smoking status was performed. Alcohol, meat, fish and fruit and vegetable consumption are suspected to have an influence on different cancers; thus we decided to perform stratified analysis on high versus low consumption of the above-mentioned variables as well. We found no overall relationship between cancers and the six different dietary patterns in this study. When stratifying on alcohol consumption, fruit and vegetable consumption and fatty fish consumption, there was a statistically higher risk of total cancer and breast cancer with high alcohol consumption, and a significantly higher risk of breast cancer with low consumption of fruit and vegetables or with low consumption of fatty fish in the western group only. A significantly higher risk of total cancer with low intake of fatty fish in the alcohol group was also observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".