The Gap in Digestive Organ Cancers in Inner Mongolia, 2009–2012
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to explore the characteristics of digestive organ cancer mortality and the potential years of life lost in Inner Mongolia, and to provide evidence for the prevention of digestive organ cancers. METHODS: Using data from the Death Registry System from 2009 to 2012, we classified male and female cancer deaths according to the International Classification of Disease (10th revision). The mortality and potential years of life lost were calculated for digestive organ cancers in Inner Mongolia. The average years of life lost was calculated. RESULTS: Digestive organ cancer mortality in Inner Mongolia was higher in men than in women. The potential years of life lost were also much higher in men than in women. Gallbladder cancer, pancreatic cancer, and colorectal, anus, and anal canal cancer were the most prominent contributors to mortality. Esophageal cancer was the most prominent contributor to potential years of life lost, and was the leading cause of average years of life lost in both sexes. CONCLUSION: Liver cancer and stomach cancer mortality and the potential years of life lost to liver and stomach cancer are demonstrably higher in Inner Mongolia. Although esophageal cancer mortality was not the highest of the digestive organ cancers, the average years of life lost to esophageal was the highest for both sexes, and it should therefore be targeted for prevention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".