Association between Dietary Energy Density and Risk of Breast, Endometrial, Ovarian, and Colorectal Cancer among Canadian Women
Bibliographic record
Abstract
Abstract Background: Dietary energy density (DED) is strongly associated with cancer-associated metabolic disorders such as obesity and metabolic syndrome and may thus influence carcinogenesis. However, little is known about its association with cancer. Therefore, we investigated the association of DED with risk of breast, endometrial, ovarian, and colorectal cancers in the Canadian Study of Diet, Lifestyle, and Health. Methods: We conducted a case–cohort study that included an age-stratified subcohort of 3,120 of the 39,532 female participants who completed self-administered lifestyle and dietary questionnaires at baseline, and in whom, respectively, 922, 188, 104, and 269 incident breast, endometrial, ovarian, and colorectal cancer cases were diagnosed, respectively. We estimated HRs and 95% confidence intervals for the association of DED with risk of these cancers using Cox proportional hazards regression models modified for the case–cohort design. Results: There was no statistically significant association between DED and risk of breast, endometrial, ovarian, and colorectal cancers. Conclusions: Our study suggests that DED is not independently associated with risk of breast, endometrial, ovarian, and colorectal cancers among women. Impact: Further investigation of the association between DED and risk of these cancers in larger prospective studies is warranted, as demonstration of associations may have important implications for primary prevention of these cancers. Cancer Epidemiol Biomarkers Prev; 27(3); 338–41. ©2017 AACR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".