Associations of pre‐existing co‐morbidities with skeletal muscle mass and radiodensity in patients with non‐metastatic colorectal cancer
Bibliographic record
Abstract
Abstract Background and aim Co‐morbidities and computerized tomography‐measured muscle abnormalities are both common in cancer patients and independently adversely influence clinical outcomes. Muscle abnormalities are also evident in other diseases, such as diabetes and obesity. This study examined for the first time the association between co‐morbidities and muscle abnormalities in patients diagnosed with colorectal cancer (CRC). Methods This cross‐sectional study included 3051 non‐metastatic patients with Stages I–III CRC. Muscle abnormalities, measured at diagnosis, were defined as low skeletal muscle mass index (SMI) or low skeletal muscle radiodensity (SMD) quantified using computerized tomography images using optimal stratification. Co‐morbidities included in the Charlson index were ascertained. χ2tests were used to compare the prevalence of co‐morbidities by the presence or absence of each muscle abnormality. Logistic regressions were performed to evaluate which co‐morbidities predicted muscle abnormalities adjusting for age, sex, body mass index, weight change, cancer stage, cancer site, race/ethnicity, and smoking. Results Mean age was 63 years; 50% of patients were male. The prevalence of low SMI and low SMD were 43.1% and 30.2%, respectively. Co‐morbidities examined were more prevalent in patients with low SMD than in those with normal SMD, and most remained independent predictors of low SMD after adjustment for covariates. Co‐morbidities associated with higher odds of low SMD included myocardial infarction [odds ratio (OR) = 1.77,P = 0.023], congestive heart failure (OR = 3.27,P < 0.001), peripheral vascular disease (OR = 2.15,P = 0.002), diabetes with or without complications (OR = 1.61,P = 0.008; OR = 1.46,P = 0.003, respectively), and renal disease (OR = 2.21,P < 0.001). By contrast, only diabetes with complications was associated with lower odds of low SMI (OR = 0.64,P = 0.007). Conclusions Prevalence of muscle abnormalities was high in patients with non‐metastatic CRC. Pre‐existing co‐morbidities were associated with low SMD, suggestive of a potential shared mechanism between fat infiltration into muscle and each of these co‐morbidities.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".