Gender differences in colorectal cancer incidence, mortality, hospitalizations and surgical procedures in Canada
Bibliographic record
Abstract
BACKGROUND: Over the past few decades there have been changes in incidence and mortality of colorectal cancer. OBJECTIVE: To examine gender differences in incidence, hospitalization, hospital-based procedures and mortality for colorectal cancer. METHODS: Data were derived from the Hospital Morbidity Database, Canadian Cancer Registry and the Canadian Mortality Database. RESULTS: Overall incidence and mortality rates for colorectal cancer are decreasing, but remain substantially higher for males. Absolute numbers of cases are similar for men and women. The top subsite for men was rectal cancer, which was third highest for women, whereas right colon cancer was highest for women. Male/female ratios for incidence and surgeries were highest for distal cancer and are increasing with time. CONCLUSIONS: Although overall incidence rates have shown a decline, absolute numbers of new colorectal cancer cases have increased. While men have higher colorectal cancer rates, women have similar numbers and screening should target both equally. Over the years, colorectal cancer subsites are showing a rightward shift, i.e. an increase in proximal subsites, but a leftward shift in male/female ratios, i.e. a greater decrease for the more distal subsites in females. The lower rates for women for distal cancer are compatible with a degree of hormonal protection based on oral contraceptive and hormone replacement therapy. Colorectal cancer will continue to be a considerable public health problem in the foreseeable future.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".