Global trends in colorectal cancer mortality: projections to the year 2035
Bibliographic record
Abstract
Colorectal cancer (CRC) is the third most common cancer worldwide and the fourth most common cause of cancer death. Predictions of the future burden of the disease inform health planners and raise awareness of the need for cancer control action. Data from the World Health Organization (WHO) mortality database for 1989-2016 were used to project colon and rectal cancer mortality rates and number of deaths in 42 countries up to the year 2035, using age-period-cohort (APC) modelling. Mortality rates for colon cancer are predicted to continue decreasing in the majority of included countries from Asia, Europe, North America and Oceania, except Latin America and Caribbean countries. Mortality rates from rectal cancer in general followed those of colon cancer, however rates are predicted to increase substantially in Costa Rica (+73.6%), Australia (+59.2%), United States (+27.8%), Ireland (+24.2%) and Canada (+24.1%). Despite heterogeneous trends in rates, the number of deaths is expected to rise in all countries for both colon and rectal cancer by 60.0% and 71.5% until 2035, respectively, due to population growth and ageing. Reductions in colon and rectal cancer mortality rates are probably due to better accessibility to early detection services and improved specialized care. The expected increase in rectal cancer mortality rates in some countries is worrisome and warrants further investigations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".