The Association of Abdominal Adiposity With Mortality in Patients With Stage I–III Colorectal Cancer
Bibliographic record
Abstract
BACKGROUND: The quantity and distribution of adipose tissue may be prognostic measures of mortality in colorectal cancer patients, and such associations may vary by patient sex. METHODS: This cohort included 3262 stage I-III colorectal cancer patients. Visceral and subcutaneous adipose tissues were quantified using computed tomography. The primary endpoint was all-cause mortality. Restricted cubic splines estimated statistical associations with two-sided P values. RESULTS: Visceral adipose tissue was prognostic of mortality in a reverse L-shaped pattern (nonlinear P = .02); risk was flat to a threshold (∼260 cm2) then increased linearly. Subcutaneous adipose tissue was prognostic of mortality in a J-shaped pattern (nonlinear P < .001); risk was higher at extreme (<50 cm2) but lower at intermediate values (>50 to ≤560 cm2). Patient sex modified the prognostic associations between visceral adipose tissue (Pinteraction = .049) and subcutaneous adipose tissue (Pinteraction = .04) with mortality. Among men, visceral adiposity was associated with mortality in a J-shaped pattern (nonlinear P = .003), whereas among women, visceral adiposity was associated with mortality in a linear pattern (linear P = .008). Among men, subcutaneous adiposity was associated with mortality in an L-shaped pattern (nonlinear P = .01), whereas among women, subcutaneous adiposity was associated with mortality in a J-shaped pattern (nonlinear P < .001). CONCLUSIONS: Visceral and subcutaneous adipose tissue were prognostic of mortality in patients with colorectal cancer; the shape of these associations were often nonlinear and varied by patient sex. These results offer insight into the potential biological mechanisms that link obesity with clinical outcomes in patients with cancer, suggesting that the dysregulated deposition of excess adiposity is prognostic of mortality.
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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 | 0.000 | 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".