Intake of dietary fibre and lifetime non-steroidal anti-inflammatory drug (NSAID) use and the incidence of colorectal polyps in a population screened for colorectal cancer
Bibliographic record
Abstract
BACKGROUND: There is suggestive evidence that increased intake of dietary fibre and the use of non-steroidal anti-inflammatory drugs (NSAIDs) are generally associated with decreased colorectal cancer risk. However, the effects on precursors of colorectal cancer, such as adenomatous polyps, are mixed. We present the associations between dietary fibre intake and NSAID use on the presence and type of colorectal polyps in a screening population. METHODS: A cross-sectional study of 2548 individuals undergoing colonoscopy at the Forzani & MacPhail Colon Cancer Screening Centre (Calgary, Canada) was conducted. Dietary fibre intake and NSAID use were assessed using the Diet History Questionnaire I or II and the Health and Lifestyle Questionnaire. Colorectal outcomes were documented as a polyp or high-risk adenomatous polyp (HRAP; villous histology, high-grade dysplasia, ≥10 mm or ≥3 adenomas). Crude and ORs and 95% CIs were estimated using unconditional logistic regression. RESULTS: There were 1450 negative colonoscopies and 1098 patients with polyps, of which 189 patients had HRAPs. Total dietary fibre intake was associated with a decreased presence of HRAPs (OR=0.50, 95% CI: 0.29 to 0.86) when comparing the highest to lowest quartiles and was observed with both soluble (OR=0.51, 95% CI: 0.30 to 0.88) and insoluble (OR=0.51, 95% CI: 0.30 to 0.86) fibres. Ever use of NSAIDs was also inversely associated with HRAPs (OR=0.65, 95% CI: 0.47 to 0.89), observed with monthly (OR=0.60, 95% CI: 0.37 to 0.95) and daily (OR=0.53, 95% CI: 0.32 to 0.86) use. CONCLUSIONS: Dietary fibre intake and NSAID use were associated with a decreased risk of having a HRAP at screening.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".