Hypothesis and data-driven dietary patterns and colorectal Cancer survival: findings from Newfoundland and Labrador colorectal Cancer cohort
Bibliographic record
Abstract
Abstract Background Dietary patterns are commonly used in epidemiological research, yet there have been few studies assessing if and how research results may vary across dietary patterns. This study aimed to estimate the risk of mortality/recurrence/metastasis using different dietary patterns and comparison amongst the patterns. Methods Dietary patterns were identified by Cluster Analysis (CA), Principal Component Analysis (PCA), Alternate Mediterranean Diet score (altMED), Recommended Food Score (RFS) and Dietary Inflammatory Index (DII) scores using a 169-item food frequency questionnaire. Five hundred thirty-two colorectal cancer patients diagnosed between 1999 and 2003 in Newfoundland were followed-up until 2010. Overall Mortality (OM) and combined Mortality, Recurrence or Metastasis (cMRM) were identified. Comparisons were made with adjusted Cox proportional Hazards Ratios (HRs), correlation coefficients and the distributions of individuals in defined clusters by quartiles of factor and index scores. Results One hundred and seventy cases died from all causes and 29 had a cancer recurrence/metastasis during follow-up. Processed meats as classified by PCA (HR 1.82; 95% confidence interval (CI) 1.07–3.09), clusters characterized by meat and dairy products (HR 2.19; 95% CI 1.03–4.67) and total grains, sugar, soft drinks (HR 1.95; 95% CI 1.13–3.37) were associated with a higher risk of cMRM. Poor adherence to AltMED increased the risk of all-cause OM (HR 1.62; 95% CI 1.04–2.56). Prudent vegetable, high sugar pattern, RFS and DII had no significant association with both OM and cMRM. Conclusion Estimation of OM and cMRM varied across dietary patterns which is attributed to the differences in the foundation of each pattern.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".