Gender differences in outcomes for early-stage colon cancer.
Bibliographic record
Abstract
605 Background: Retrospective analyses of clinical trials suggest that gender may influence the risk of recurrence and survival in patients diagnosed with early stage colon cancer. Whether this association persists in the setting of routine clinical practice is unclear. Our aims were to 1) assess for gender differences in outcomes in early stage colon cancer and 2) evaluate if the effect of gender on outcomes is modified by adjuvant therapy (AT). Methods: Using a population-based cohort of patients diagnosed with stage II or III colon cancer and referred to any 1 of 5 regional cancer centers in British Columbia, Canada from 2001 to 2005, we compared 5-year outcomes between men and women. Multivariate Cox proportional hazard models were constructed to explore the relationships between gender and prognosis, while controlling for patient and tumor characteristics. Interaction terms between gender and AT were used to examine the predictive value of gender. Results: We included 1837 patients: 970 men and 867 women. Baseline characteristics were similar: median ages were 68 and 69 years; 39 and 38% had stage II colon cancer; and 46 and 44% received AT, respectively. Outcomes were better in women than in men (5-year cancer-specific survival rate 73 vs. 71% and 5-year overall survival rate 68 vs. 62%). In Cox regression models, women continue to experience better overall prognosis (HR for death = 0.80, 95% CI 0.69-0.92, p=0.003) when compared to men. Advanced age, stage III disease, and lack of AT also correlated with worse outcomes (all p<0.01). The effect of gender on outcomes remained similar regardless of AT (HR 0.86 among women with AT and HR 0.75 among women without AT, interaction p=0.35). Conclusions: Gender is a prognostic, but not a predictive, factor for this cohort of patients with early stage colon cancer. Studies to investigate the biological mechanisms underlying this gender difference in outcomes are warranted.
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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 | 0.001 |
| 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) | 0.002 | 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".