Examining variations in body composition among patients with colorectal cancer according to site and disease stage
Bibliographic record
Abstract
Abstract Patients with colorectal cancer (CRC) often exhibit changes in body composition (BC) which are associated with poorer clinical outcomes. Many studies group colon and rectal cancers together, irrespective of staging, potentially affecting assessment and treatment strategies. Our study aimed to compare BC in patients with CRC focusing on tumor location and metastasis presence. A total of 635 individuals were evaluated, with a mean age of 61.8 ± 12.4 years and 50.2% female. The majority had rectal cancer as the primary cancer site (51.0%), and 23.6% had metastatic disease. The first regression model showed tumor site and metastasis as independent factors influencing skeletal muscle (SM), skeletal muscle index (SMI), and visceral adipose tissue variability (all p values < 0.05). The second model, adjusted for BMI, indicated tumor site as the primary factor affecting SMI variations (adjusted R 2 = 0.50 p < 0.001), with colon tumors inversely associated with SM (standardized β − 2.15(− 3.3; − 0.9) p < 0.001). A third model, considering all the confounders from the directed acyclic graphs, was constructed and the found association remained independent. Our findings highlight significant BC variations in patients with CRC, influenced by tumor location and metastases presence, underscoring the need for location-specific assessment in CRC management.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".