Diet and vitamin or mineral supplementation and risk of colon cancer by subsite in Canada
Bibliographic record
Abstract
The study assesses the association of diet and vitamin or mineral supplementation with risk of proximal or distal colon cancer. Mailed questionnaires were completed by 1723 newly diagnosed, histologically confirmed colon cancer cases and 3097 population controls between 1994 and 1997 in seven Canadian provinces. Measurement included information on socio-economic status, physical activity, smoking habits, alcohol use, diet and vitamin or mineral supplementation. Odds ratios and 95% confidence intervals were derived through unconditional logistic regression. Linear regression was used to examine that dietary factors affect body mass index. The strongest positive associations between colon cancer risk and increasing total fat intake were observed for proximal colon cancer in men and for distal colon cancer in both men and women. Increased consumption of vegetables, fruit and whole-grain products did not reduce the risk of colon cancer. A modest reduction in distal colon cancer risk was noted in women who consumed yellow-orange vegetables. Significant positive associations were observed between proximal colon cancer risk in men and consumption of red meat and dairy products, and between distal colon cancer risk in women and total intake of meat and processed meat. We also saw strong associations between bacon intake and both subsites of colon cancer in women. When men were compared with women directly by subsite however, the results did not show a corresponding association. A significantly reduced risk of distal colon cancer was noted in women only with increasing intake of dairy products and of milk. Among men and women taking vitamin and mineral supplements for more than 5 years, significant inverse associations with colon cancer were most pronounced among women with distal colon cancer. These findings suggest that dietary risk factors for proximal colon cancer may differ from those for distal colon cancer.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".