The influence of social and psychological factors on the relationship between body composition and colon cancer outcomes
Bibliographic record
Abstract
The current five-year survival rate for colorectal cancer in Canada is 65%, which is influenced by well-established factors (e.g., age, cancer stage). There is also consistent observational research that higher body mass or social factors (e.g., living alone) can negatively impact survival. This thesis project examined the association between body composition at diagnosis and relapse-free survival (RFS) at three years post-diagnosis, and how these relationships may be influenced by social or psychological factors at diagnosis in individuals with colon cancer. Methods: A cohort of individuals treated for stage III colon cancer at BC Cancer from 2012 to 2015 with clinical and demographic data available was created. CT scans of the third lumbar vertebra at diagnosis were analyzed to determine skeletal muscle index (SMI) (muscle cross-sectional area normalized for height), sarcopenia (using published SMI cut-off points), skeletal muscle density (SMD) (average attenuation of muscle), and skeletal muscle gauge (SMG) (SMI multiplied by SMD). Social and psychological factors were obtained at diagnosis and included social isolation, patient-reported concerns, and symptoms of anxiety and depression from BC Cancer’s Psychosocial Screen for Cancer-Revised, and community size and neighbourhood income based on individuals’ postal codes. Multivariable logistic regression models were used to examine: 1) The associations of SMI, SMD, SMG, and sarcopenia with RFS; 2) How social and psychological factors (selected using variable visualization and univariable regression) influenced the relationships. Results: Individuals were a median age of 62.0 years and 51.1% were male. Individuals with a lower SMD (OR= 0.97, 95% CI= 0.95,0.997), lower SMG (for a 100-unit change, OR= 0.93, 95% CI= 0.88,0.98), or sarcopenia (OR= 1.80, 95% CI= 1.06,3.10) had greater odds of having a relapse. This association was influenced by social isolation; for any given SMD, SMG, or sarcopenia status, individuals with one or more markers of social isolation had approximately two times greater odds of having a relapse than individuals without markers of social isolation. Conclusion: Consistent with the literature, sarcopenia was associated with RFS, as was SMD and SMG, measures for which there is less evidence surrounding their relationship with long-term outcomes. Social isolation appeared to influence these relationships.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".