Smoking, alcohol, and colon cancer survival are modified by immune biomarkers: a population-representative study
Bibliographic record
Abstract
Lifestyle factors such as smoking and alcohol may influence colon cancer (CC) survival, but it is unclear whether their effects vary by tumour-infiltrating immune biomarkers. This study examined CC-specific survival by smoking and alcohol status, stratified by immune cell density, in a large population-based cohort. The study included 661 individuals who underwent surgery for stage II or III CC between 2004 and 2008 within two Health and Social Care (HSC) Trusts in Northern Ireland. Representative formalin-fixed, paraffin-embedded (FFPE) tumour blocks were retrieved, and immunohistochemistry (IHC) was performed on tissue microarrays constructed from both the central tumour and the invasive margin. Cox proportional hazards models were used to assess CC-specific survival, adjusting for key clinical and demographic confounders. Ever smoking, compared to never smoking, was associated with poorer CC-specific survival among individuals with lower densities of CD3+, CD4 +, and FOXP3 + tumour-infiltrating immune cells. Among those with higher CD8 + cell density in the central tumour, ever smoking was linked to worse outcomes. Similar patterns were seen in the invasive margin, although these were not all statistically significant. No significant associations were observed between alcohol use and survival across any immune biomarker subgroups. Smoking was associated with poorer survival among patients with CC, and this association appears to be modified by the density of tumour-infiltrating immune cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".