Gastrointestinal cancer mortality rate global trends over the last century.
Bibliographic record
Abstract
457 Gastrointestinal Cancer Mortality Rate Global Trends Over the Last Century Background: Global gastroenterology cancer mortality trends have drastically changed over past decades due to alterations in risk factors such as diet, populations, exposures, and medical advancements. This study investigates specifically how meat consumption, the biggest evolving risk factor for GI cancer mortality, has influenced various gastrointestinal incidence rates globally. Although the correlation between meat consumption and cancer risk has been investigated, a global temporal study investigating the national mortality rates of gastrointestinal cancers relating to meat production remains unexplored. We researched causes for the trends between meat production and GI cancer mortality in the USA, Canada, Japan, France, and Singapore. Methods: Cancer mortality data was collected from the WHO Cancer Mortality Database, specifically the IARC database. Meat consumption data was unavailable between the 1960s-2010, so meat production data was used and obtained from the FAOSTAT. Results: The pancreatic cancer mortality rate increased in each country except Canada. This spike is due to increased meat consumption, obesity, lack of screening modalities, poor prognosis, and late diagnosis of the disease. Canada’s 1% drop in mortality rate can be attributed to a decreased smoking rate amongst men (62% to 16% from 1965-2017) as well as a decline in meat consumption. The mortality rates of gastric and colorectal cancer (CRC) have decreased despite a meat production increase. Decreased H. pylori prevalence (Europe: 48.8% to 39.8%, North America: 42.4% to 26.6%, and Western Asia: 53.6% to 54.3%) , better food preservation, and improvement in environmental conditions have lowered gastric cancer incidence. The CRC mortality rate in the USA, Canada, Japan, and France decreased mostly due to colonoscopy screening measures, better treatment, and decreased red meat consumption. In Singapore, increased obesity and high caloric diets account for an increased CRC mortality rate despite decreased meat production. This rate is exacerbated by lower screening rates due to decreased CRC risk awareness. Conclusions: Gastric and CRC mortality rates decreased despite increased meat production, while pancreatic cancer incidence rates have increased. These trends are further investigated and necessary to understand to lower the mortality rates of GI cancers on a global scale.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".