Thyroid cancer “epidemic” also occurs in low‐ and middle‐income countries
Bibliographic record
Abstract
Thyroid cancer incidence varies greatly between and within high-income countries (HICs), and overdiagnosis likely plays a major role in these differences. Yet, little is known about the situation in low- and middle-income countries (LMICs). We compare up-to-date thyroid cancer incidence and mortality at national and subnational levels. 599,851 thyroid cancer cases in subjects aged 20-74 reported in Cancer Incidence in Five Continents volume XI from 55 countries with at least 0.5 million population, aged 20-74 years, covered by population-based cancer registration, and 22,179 deaths from the WHO Mortality Database for 36 of the selected countries, over 2008-2012, were included. Age-standardized rates were computed. National incidence rates varied 50-fold. Rates were 4 times higher among women than men, with similar patterns between countries. The highest rates (>25 cases per 100,000 women) were observed in the Republic of Korea, Israel, Canada, the United States, Italy, France, and LMICs such as Turkey, Costa Rica, Brazil, and Ecuador. Incidence rates were low (<8) in a few HICs (the Netherlands, the United Kingdom, and Denmark) and lowest (3-4) in some LMICs (such as Uganda and India). Within-country incidence rates varied up to 45-fold, with the largest differences recorded between rural and urban areas in Canada (HIC) and Brazil, India, and China (LMICs). National mortality rates were very low (<2) in all countries and in both sexes, and highest in LMICs. The very high thyroid cancer incidence and low mortality rates in some LMICs also strongly suggest a major role of overdiagnosis in these countries.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 0.001 |
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".