Body Composition, Relative Dose Intensity, and Adverse Events among Patients with Colon Cancer
Bibliographic record
Abstract
BACKGROUND: Despite evidence that low muscle increases the risk of chemotoxicity, most chemotherapies are dosed on body surface area without considering body composition. Among 178 patients with colon cancer, we assessed muscle and adipose tissue with multiple techniques and examined their associations with relative dose intensity (RDI) and adverse events. METHODS: We estimated (i) cross-sectional skeletal muscle area (SMA) and total adipose tissue (TAT) area at L3 from computed tomography (CT); (ii) appendicular lean mass (ALM) and total body fat (TBF) mass from dual-energy X-ray absorptiometry (DXA); and (iii) total body skeletal muscle mass using D3-creatine (D3Cr) dilution. We standardized each measurement by its sex-specific standard deviation (SD). The primary outcome was reduced RDI (RDI <85%). The secondary outcome was the number of moderate and severe adverse events during each cycle of chemotherapy. We estimated the associations of muscle and adipose tissue measurements (per SD increase) with reduced RDI using logistic regression and adverse events using generalized estimating equations for repeated measures. RESULTS: Higher CT SMA and DXA ALM were significantly associated with a lower risk of reduced RDI [odds ratios: 0.56 (0.38-0.81) for CT SMA; 0.56 (0.37-0.84) for DXA ALM]. No measurements of muscle or adipose tissue were associated with adverse events. CONCLUSIONS: More muscle was associated with improved chemotherapy completion among patients with colon cancer, whereas muscle and adipose tissue were not associated with adverse events. IMPACT: Considering body composition may help personalize dosing for colon cancer chemotherapy by identifying patients at risk for poor chemotherapy outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".