GLOBAL CANCER STATISTICS 2018: GLOBOCAN ESTIMATES OF INCIDENCE AND MORTALITY WORLDWIDE STOMACH CANCERS AND THEIR RELATIONSHIP WITH THE HUMAN DEVELOPMENT INDEX (HDI)
Bibliographic record
Abstract
Objective: Stomach cancer is one of the most common cancers in the worldwide and the second most common cause of cancer-induced deaths after lung cancer. The aim of this study was to investigate epidemiology of stomach cancer incidence and mortality in 185 countries and its relationship with HDI index in 2018. Materials and Methods: This study is a descriptive-analytic study that is based on extraction of cancer incidence and mortality data from World Bank Cancer in 2018. The incidence and mortality rates and stomach cancer distribution maps were drawn for world countries. To analyze data, correlation test and regression tests were used to evaluate the correlation between incidence and mortality with HDI. The statistical analysis was carried out by Stata-14 and the significance level was estimated at the level of 0.05. Results: Stomach cancer, with 1033701 cases (5.7 of all cancers), was the fifth most common cancer in 2018, with the highest incidence and mortality related to the Asia continent and Eastern Asia region. There was a positive and significant correlation between incidence of stomach cancer and HDI index (R=0.218, p<0.05). While the correlation between stomach cancer mortality index and HDI (R=0.008, p>0.05) was not statistically significant. Also, there was a positive and significant correlation between the incidence of stomach cancer with MYS (r=0.19, p<0.05), LEB (r=0.22, p<0.05) and EYS (r=0.25, p<0.05) and there was a negative and significant correlation with GNI (r=-0.19, p<0.05). Conclusions: Considering that stomach cancer is the second leading cause of death worldwide, it is important to investigate the risk factors of this disease in the countries of the world. According to the results of this study, paying attention to the development index can be effective in reducing the mortality rate of stomach cancer.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".