Effect of changes in body composition and sociodemographic factors on colon cancer survival.
Bibliographic record
Abstract
296 Background: Muscle mass, muscle quality, and adipose tissue volumes have been shown to affect colon cancer survival. Further research is required to understand how change in body composition during treatment impacts colon cancer outcomes, and how covariables such mental health and socioeconomic status affect this relationship. The aim of this project was to examine how changes in body composition during chemotherapy for colon cancer impact the duration of disease-free survival (DFS) and how sociodemographic covariables (age, sex, social isolation, depression, anxiety, income, community size) influence this relationship. Methods: Computed tomography (CT) scans from the time of diagnosis and the end of chemotherapy were obtained from individuals treated for stage III colon cancer with oxaliplatin at BC Cancer between 2012 and 2016. Whole body tissue volumes were estimated based on CT images at the level of the third lumbar vertebra, analysed using DAFS Express (Voronoi Health Analytics Inc., Vancouver BC). Muscle quantity was measured as skeletal muscle index (SMI), and muscle quality was measured as skeletal muscle density (SMD) and skeletal muscle gauge (SMG). Quantity of visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intermuscular adipose tissue (IMAT), and total adipose tissue (TAT) were also measured. Social isolation, anxiety, and depression symptoms were measured using the validated Psychosocial Screen for Cancer–Revised. Neighbourhood income and community population were estimated using postal codes. Cox proportional hazard models were calculated for the effect of body composition variables on DFS, and interactions with sociodemographic variables. Results: Significant reductions in SMI, SMD, SMG, VAT, IMAT and TAT were observed between diagnosis and post-chemotherapy (n=282). Improved survival was associated with higher SMD and lower IMAT at diagnosis and post-chemotherapy, lower VAT at diagnosis, and an increase in VAT or TAT during chemotherapy. Body composition had a more significant effect on survival in individuals who were older, female, or lived in smaller communities. Conclusions: Significant reductions in SMI, SMD, SMG, VAT, IMAT and TAT were observed between diagnosis and post-chemotherapy (n=282). Improved survival was associated with higher SMD and lower IMAT at diagnosis and post-chemotherapy, lower VAT at diagnosis, and an increase in VAT or TAT during chemotherapy. Body composition had a more significant effect on survival in individuals who were older, female, or lived in smaller communities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".