Epidemiology Incidence and Mortality Worldwide Common cancers in males and Their Relationship with the Human Development Index (HDI): An Ecological Study Updated in the World
Bibliographic record
Abstract
This article provides a status report on the global burden of common cancers in male in worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality. Based on the results of cancer records in 2018, 9456418 cases of malignancy were recorded in males, with cancer (1368524 cases, 14.5 %), prostat cancer (1276106 cases, 13.5%), colorectal cancer(1026215 cases, 10.9%), stomach cancer(683754 cases, 7.2%) and liver cancer (596574 caces, 6.3%) were the five most common cancers in men worledwide. the total of deaths due to cancer in human in 2018 is 5385640. the five cases of death due to cancer in men worldwide are lung cancer (1184947 case, 22%), liver (548375 cases, 2.10%), stumach cancer (513555 cases, 9.5%), colorectal cancer (484224 case, 9%) and prostat cancer (358989 cases, 7.7 %). our resulth showed that there was a positive correlation between the incidence of lung cancer ( r= 0.629, p<0.0001), prostat cancer (r= 0.534, p<0.0001), colorectal cancer (r=0.745, p<0.0001) and stomach cancer (r=0.268, p<0.0001) with HDI index, while there was no significant relationship between liver cancer and the HDI (r=0.079, p>0.05). the results also showed that there was a positive and significant correlation between mortality from lung cancer (r=0.632, p<0.0001) and colorectal (r=0.627, p<0.0001) with HDI, wereas this correlation was negative for prostate cancer (r=-0.187, p<0.05).
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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 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".