Dietary patterns and colorectal cancer recurrence and survival: a cohort study
Bibliographic record
Abstract
OBJECTIVE: To examine the association between dietary patterns and colorectal cancer (CRC) survival. DESIGN: Cohort study. SETTING: A familial CRC registry in Newfoundland. PARTICIPANTS: 529 newly diagnosed CRC patients from Newfoundland. They were recruited from 1999 to 2003 and followed up until April 2010. OUTCOME MEASURE: Participants reported their dietary intake using a food frequency questionnaire. Dietary patterns were identified with factor analysis. Multivariable Cox proportional hazards models were employed to estimate HR and 95% CI for association of dietary patterns with CRC recurrence and death from all causes, after controlling for covariates. RESULTS: Disease-free survival (DFS) among CRC patients was significantly worsened among patients with a high processed meat dietary pattern (the highest vs the lowest quartile HR 1.82, 95% CI 1.07 to 3.09). No associations were observed with the prudent vegetable or the high-sugar patterns and DFS. The association between the processed meat pattern and DFS was restricted to patients diagnosed with colon cancer (the highest vs the lowest quartile: HR 2.29, 95% CI 1.19 to 4.40) whereas the relationship between overall survival (OS) and this pattern was observed among patients with colon cancer only (the highest vs the lowest quartile: HR 2.13, 95% CI 1.03 to 4.43). Potential effect modification was noted for sex (p value for interaction 0.04, HR 3.85 for women and 1.22 for men). CONCLUSIONS: The processed meat dietary pattern prior to diagnosis is associated with higher risk of tumour recurrence, metastasis and death among patients with CRC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".