Association between Body Mass Index and Mortality for Colorectal Cancer Survivors: Overall and by Tumor Molecular Phenotype
Bibliographic record
Abstract
BACKGROUND: Microsatellite instability (MSI) and BRAF mutation status are associated with colorectal cancer survival, whereas the role of body mass index (BMI) is less clear. We evaluated the association between BMI and colorectal cancer survival, overall and by strata of MSI, BRAF mutation, sex, and other factors. METHODS: This study included 5,615 men and women diagnosed with invasive colorectal cancer who were followed for mortality (maximum: 14.7 years; mean: 5.9 years). Prediagnosis BMI was derived from self-reported weight approximately one year before diagnosis and height. Tumor MSI and BRAF mutation status were available for 4,131 and 4,414 persons, respectively. Multivariable hazard ratios (HR) and 95% confidence intervals (CI) were estimated from delayed-entry Cox proportional hazards models. RESULTS: In multivariable models, high prediagnosis BMI was associated with higher risk of all-cause mortality in both sexes (per 5-kg/m(2); HR, 1.10; 95% CI, 1.06-1.15), with similar associations stratified by sex (Pinteraction: 0.41), colon versus rectum (Pinteraction: 0.86), MSI status (Pinteraction: 0.84), and BRAF mutation status (Pinteraction: 0.28). In joint models, with MS-stable/MSI-low and normal BMI as the reference group, risk of death was higher for MS-stable/MSI-low and obese BMI (HR, 1.32; P value: 0.0002), not statistically significantly lower for MSI-high and normal BMI (HR, 0.86; P value: 0.29), and approximately the same for MSI-high and obese BMI (HR, 1.00; P value: 0.98). CONCLUSIONS: High prediagnosis BMI was associated with increased mortality; this association was consistent across participant subgroups, including strata of tumor molecular phenotype. IMPACT: High BMI may attenuate the survival benefit otherwise observed with MSI-high tumors.
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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 | 0.001 |
| 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) | 0.001 | 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".