Impact of resistance training on inflammatory biomarkers and associations with treatment outcomes in colon cancer
Bibliographic record
Abstract
INTRODUCTION: Among patients with colon cancer undergoing adjuvant chemotherapy, the impact of resistance training with supplemental dietary protein on inflammatory changes during treatment, whether baseline or changes in inflammatory markers are associated with relative dose intensity (RDI), and the associations of inflammation with body composition were investigated. METHODS: A multicenter randomized clinical trial of 174 patients with colon cancer undergoing adjuvant chemotherapy assigned to a home-based resistance training program or usual care was conducted. High-sensitivity C-reactive protein (hsCRP), interleukin-6, tumor necrosis factor-α receptor-II, and growth differentiation factor-15 levels were assessed preintervention and following chemotherapy completion. Baseline body composition was evaluated via dual-energy X-ray absorptiometry. Multivariate analyses were adjusted for sociodemographic and clinical factors. RESULTS: Patients randomized to resistance training versus usual care experienced similar changes in all inflammatory markers. Those in the highest versus lowest tertile of baseline hsCRP were more likely to have received RDI >70% (odds ratio, 4.11; 95% CI, 1.29-13.1); however, changes across any of the inflammatory markers were not associated with RDI. Patients in the highest versus lowest tertiles of hsCRP, interleukin-6, and tumor necrosis factor-α receptor-II were more likely to have higher baseline body mass index, total lean mass, and total fat mass. CONCLUSION: Inflammatory markers in patients with colon cancer undergoing adjuvant chemotherapy were not significantly impacted by randomization to a resistance training program but were associated with baseline body composition measures. Further investigations are needed to better elucidate the potential role of inflammatory markers and body composition in predicting important treatment outcomes. CLINICALTRIALS: GOV: NCT03291951.
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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 | 0.000 |
| 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".