Cigarette Smoke Increases Risk for Colorectal Neoplasia in Inflammatory Bowel Disease
Bibliographic record
Abstract
BACKGROUND & AIMS: Patients with inflammatory bowel disease are at increased risk of colorectal neoplasia (CRN) due to mucosal inflammation. As current surveillance guidelines form a burden on patients and healthcare costs, stratification of high-risk patients is crucial. Cigarette smoke reduces inflammation in ulcerative colitis (UC) but not Crohn's disease (CD) and forms a known risk factor for CRN in the general population. Due to this divergent association, the effect of smoking on CRN in IBD is unclear and subject of this study. METHODS: In this retrospective cohort study, 1,386 IBD patients with previous biopsies analyzed and reported in the PALGA register were screened for development of CRN. Clinical factors and cigarette smoke were evaluated. Patients were stratified for guideline-based risk of CRN. Cox-regression modeling was used to estimate the effect of cigarette smoke and its additive effect within the current risk stratification for prediction of CRN. RESULTS: 153 (11.5%) patients developed CRN. Previously described risk factors, i.e. first-degree family member with CRN in CD (p-value=.001), presence of post-inflammatory polyps in UC (p-value=.005), were replicated. Former smoking increased risk of CRN in UC (HR 1.73; 1.05-2.85), whereas passive smoke exposure yielded no effect. For CD, active smoking (2.20; 1.02-4.76) and passive smoke exposure (1.87; 1.09-3.20) significantly increased CRN risk. Addition of smoke exposure to the current risk-stratification model significantly improved model fit for CD. CONCLUSIONS: This study is the first to describe the important role of cigarette smoke in CRN development in IBD patients. Adding this risk factor improves the current risk stratification for CRN surveillance strategies.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".