S288 Temporal Trends in the Mortality of Colorectal Cancer in Canada Over the Past 3 Decades
Bibliographic record
Abstract
Introduction: Colorectal cancer (CRC) imposes significant global health and socioeconomic burden due to the associated morbidity and mortality. Evaluation for temporal trends in CRC-related mortality is of paramount importance as it enables the identification for the need of any prompt intervention, in addition to forming the foundational basis for any health policy modifications targeting the resultant burden. Methods: The temporal trends of CRC-related mortality in Canada over the past 3 decades have been evaluated by retrieving the relevant data from the Global Burden of Diseases 2019 database. Subsequently, Joinpoint Regression Analysis software was utilized to calculate the Annual Percentage Change (APC) and the Average Annual Percentage Change (AAPC), stratified by gender and age. Results: A total of 263,166 CRC-related deaths were reported in Canada over the past 3 decades with a male predominance of 51%. Gender stratification demonstrated contrasting variations among males and females. Males underwent a statistically significant increment in CRC-related mortality with an AAPC of 0.73 (95%CI 0.66 to 0.80; P < 0.001), whereas females witnessed a statistically significant decline in CRC-related mortality with an AAPC of -0.11 (95%CI -0.18 to -0.04; P < 0.005) (Figure 1). Upon age stratification, a statistically significant increase in CRC-related mortality was observed across all age groups. Notably, individuals aged 15-49 years old observed the most prominent incline with an AAPC of 1.00 (95%CI 0.87 to 1.16; P < 0.001), followed by individuals aged 75 years and older who had an AAPC of 0.36 (95%CI 0.28 to 0.44; P < 0.001). The 50-74 years old age group observed the slightest incline in CRC-related mortality with an AAPC of 0.17 (95%CI 0.11 to 0.23; P < 0.001). Conclusion: Over the span of 3 decades, significant incline in CRC-related mortality has been observed across all age groups in Canada. However, gender stratification revealed opposing trends among males and females, with the latter experiencing significant decline in CRC-related mortality. Nonetheless, prompt measures, both at therapeutic and preventative level, are mandated to effectively tackle the increase in CRC-related mortality.Figure 1.: The Annual Percentage Change (APC) in colorectal cancer-related mortality stratified by gender in Canada over the period of 1990-2019.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".