Age-standardized mortality-to-incidence ratio for all cancers in the world
Bibliographic record
Abstract
Mortality-to-incidence ratio (MIR) is one of indicators that measure patients’ prognosis. The International Agency for Research on Cancer produces GLOBOCAN estimates of cancer incidence and mortality in 185 countries and showed the age-standardized MIRs were published in ‘Global Cancer Observatory’ website [1]. In this site, the age-adjusted MIRs were calculated by dividing the estimated age-adjusted mortality rate in 2022 by the age-adjusted incidence rate in 2022. Although the numerator and denominator of the MIR are actually different patients, it can be considered as an approximation of the proportion of cancer patients who die from cancer, with a higher value indicating a greater contribution to death from the disease (worse prognosis) and a lower value indicating a smaller contribution to death from the disease (better prognosis). To compare the estimated MIRs for all cancers among six areas (Africa, Europe, Latin America and the Caribbean, Northern America, Oceania and Asia), we plotted the age-adjusted MIRs by countries and combined areas in Fig. 1. Age-standardized mortality-to-incidence ratios for all cancers in 2022. In male, MIR in the world was 0.52, which can be interpreted that 52% of the incidence would die in cancer. All countries in Africa and many countries in Latin America and the Caribbean have MRIs higher than the 0.52 in the world (dots in the upper portion in the graph above the line), indicating a poor prognosis for cancer. In addition, all countries in Northern America (Canada and the USA), many countries in Europe tend to have a better prognosis than the world as a whole, while Asia and Oceania have larger differences by country. In Asia, among 47 countries, Japan has the lower MIR (0.33), and only three countries (Japan, Israel and the Republic of Korea) have MIRs lower than the world. MIRs in female are lower than those in male in all regions, and the size of MIR by region is essentially the same as that in male. In comparison to the MIR in the world, only that in Latin America and the Caribbean was lower than that in the world for male (better prognosis), while for women it was higher than the world (worse prognosis). Note: Data were extracted from the Global Cancer Observatory: Cancer Tomorrow [1]. The figures are prepared by the authors of this article, and the responsibility for their presentation and interpretation lies with the authors of this article. The authoes declare no conflict of interest associated with this article. None declared.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".