Is There a Sex Effect in Colon Cancer? Disease Characteristics, Management, and Outcomes in Routine Clinical Practice
Bibliographic record
Abstract
INTRODUCTION: The incidence of colon cancer varies by sex. Whether women and men show differences in extent of disease, treatment, and outcomes is not well described. We used a large population-based cohort to evaluate sex differences in colon cancer. METHODS: Using the Ontario Cancer Registry, all cases of colon cancer treated with surgery in Ontario during 2002-2008 were identified. Electronic records of treatment identified use of surgery and adjuvant chemotherapy. Pathology reports for a random 25% sample of all cases were obtained, and disease characteristics, treatment, and outcomes in women and men were compared. A Cox proportional hazards model was used to identify factors associated with overall (os) and cancer-specific survival (css). RESULTS: ≤ 0.001). Surgical procedure and lymph node yield did not differ by sex. Adjuvant chemotherapy was delivered to 18% of patients with stage ii and 64% of patients with stage iii disease; when adjusted for patient- and disease-related factors, use of adjuvant chemotherapy was similar for women and men [relative risk: 0.99; 95% confidence interval (ci): 0.94 to 1.03]. Adjusted analyses demonstrated that os [hazard ratio (hr): 0.80; 95% ci: 0.75 to 0.86] and css (hr: 0.82; 95% ci: 0.76 to 0.90) were superior for women compared with men. CONCLUSIONS: Long-term survival after colon cancer is significantly better for women than for men, which is not explained by any substantial differences in extent of disease or treatment delivered.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".