Global, regional, and national burden of thyroid cancer in young people aged 10–24 years from 1990 to 2021: an analysis based on the Global Burden of Disease Study 2021
Bibliographic record
Abstract
BACKGROUND: The burden of thyroid cancer (TC) among young people aged 10-24 years has not been systematically studied to date. This study aims to analyze the burden of TC among young people aged 10-24 years globally, regionally, and nationally from 1990 to 2021. METHODS: We collected data on the incidence, mortality, and disability-adjusted life years (DALYs) rates for TC among young people aged 10-24 years from 1990 to 2021 using the Global Burden of Disease (GBD) 2021. Joinpoint regression analysis, frontier analysis, and health inequality analysis were employed to examine the variations and changes in TC burden among young people aged 10-24 years across different countries and regions. RESULTS: From 1990 to 2021, the global burden of TC among young people has increased from 2.726 per 100,000 people [95% Uncertainty Interval (UI) 2.38-3.181] to 2.956 per 100,000 people (95% UI: 2.339-3.922), with an Average Annual Percent Change (AAPC) of 0.258 (95% Confidence Interval (CI): 0.138-0.378). The highest incidence rates were observed in Saudi Arabia, Taiwan (China), and Vietnam, while the highest mortality rates were in India, China, and Bangladesh. Frontier analysis revealed that the largest disparities in effective differences were found in the Netherlands, Germany, Canada, the United States of America, and Guinea-Bissau. The Slope Index of Inequality (SII) for DALYs increased slightly from -0.15 in 1990 to -1.3 in 2021. CONCLUSIONS: Over the past three decades, the burden of TC has increased globally among young people, particularly in poorer countries and regions. This study highlights the importance of formulating public health policies tailored to the specific circumstances of different countries and regions aimed at reducing the TC burden among young people.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".