Dietary patterns colorectal cancer risk and survival in Newfoundland, Canada
Bibliographic record
Abstract
Diet patterns commonly used in epidemiological research are derived using different methods, yet there have been few studies assessing if and how research results may vary in the same population across diet patterns. This study assesses and compares five different diet patterns identified by Principal Component Analysis (PCA), Cluster Analysis (CA), Alternate Mediterranean Diet (Alt- Med), Dietary Inflammation Index (DII), and Recommended Food Score (RFS). Colorectal cancer risk and patient’s survival is estimated using different patterns as an independent variable. Comparisons are made using hazards ratio, correlation coefficients and distributions of individuals in clusters. Disease outcome estimation varied with diet patterns used and is mainly attributed to differences in its foundation. Hazards ratios for DFS varied from 1.82; (95% CI- 1.07- 3.09) for processed meat pattern identified by PCA to HR 2.19; (95% CI 1.03-4.67) for cluster characterized by meat and dairy products and HR 1.95; (95% CI 1.13-3.37) for cluster characterized by refined grains, sugar, soft drinks. Only cluster characterized by refined grains, sugar, soft drinks had higher risk of OS (HR 2.05; 95% CI 1.18-3.57). All the diet indices showed similar null associations with both DFS and OS except Poor adherence to altMED increased the risk of all-cause mortality (HR 1.62; 95% CI 1.04- 2.56).
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".