Trends and Projections of Mortality Attributed to Occupational Neoplasms and Occupational Tracheal, Bronchus, and Lung Cancer in the World, G7 Countries and Turkey
Bibliographic record
Abstract
Background: The most important and remarkable aspect of occupational neoplasms is that they are preventable. We aimed to examine the trends and projections of mortality rates attributed to occupational neoplasms (MAON) and occupational tracheal, bronchus, and lung cancer (MAOLCa) in the world, G7 countries, and Turkey from 1990 to 2040. Methods: The study was ecological one. Data for the study were obtained from the Global Burden of Disease (GBD) Foresight Visualization. For the study, time points were set every five years. For each time point, the age-standardized MAON, MAOLCa, and their 95% confidence intervals (CIs) were recorded. Rates were analyzed by joinpoint regression analysis. Results: Globally, MAON was projected to decrease from 3.81% in 1990 to 2.83% in 2040. According to the joinpoint regression analysis, the joint year for the world was 2020. In Germany, the US, the UK, Italy, Canada and Turkey, the trend for MAON showed a decrease, similar to the global trend. However, MAON was stable in France and increased in Japan. Globally, MAOLCa was expected to decline gradually from 19.44% to 16.82% from 1990 to 2040. In the US, France and Turkey, the trend for MAOLca decreased, similar to the global trend. However, it was stable in the UK, Italy, and Canada and increased in Germany and Japan. Conclusion: MAON tended to decrease worldwide and in the six countries, except France and Japan. MAOLCa tends to decrease worldwide, in the US, France, and Turkey, increase in Germany and Japan, and remain stable in the UK, Italy, and Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".