Prediagnostic consumption of vitamin D, calcium and dairy products and colorectal cancer survival: results from the Newfoundland Colorectal Cancer Registry Cohort Study
Bibliographic record
Abstract
Abstract Vitamin D, Ca and dairy products are negatively associated with colorectal cancer (CRC) incidence, but little is known of their influence on CRC survival. To investigate prediagnostic intakes of vitamin D, Ca and dairy products for their relevance to CRC prognosis, we analysed 504 CRC patients enrolled in the Newfoundland Colorectal Cancer Registry Cohort Study who were diagnosed for the first time with CRC between 1999 and 2003. Follow-up for mortality and cancer recurrence was through April 2010. Data on diet and lifestyle factors were gathered via a validated, semi-quantitative FFQ and a Personal History Questionnaire. Multivariate Cox models estimated hazard ratios (HR) and 95 % CI for the relationship of prediagnostic intakes of vitamin D, Ca and dairy products with all-cause mortality (overall survival, OS) and disease-free survival (DFS) among CRC patients. We found that prediagnostic Ca intake from foods, but not total Ca intake, was negatively associated with all-cause mortality (HR for Q2 v. Q1, 0·44; 95 % CI, 0·26, 0·75). An inverse relationship was also seen in a dose–response fashion for prediagnostic cheese intake (HR for Q4 v. Q1, 0·57, 95 % CI, 0·34, 0·95, Ptrend = 0·029). No evidence for modification by sex, physical activity, alcohol drinking and cigarette smoking was observed. In summary, high prediagnostic intakes of cheese and Ca from foods may be associated with increased survival among CRC patients. By manipulating diet, this study may contribute to the development of novel therapies that add to the armamentarium against CRC. Replication studies are required before any nutritional interventions are made available.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".