Thyroid Cancer Incidence and Trends in United States and Canadian Pediatric, Adolescent, and Young Adults
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Thyroid cancer incidence has risen in both the United States and Canada, despite differing healthcare systems. While overdiagnosis likely partly explains this trend in adults, its impact on younger populations is unclear. We used the North American Association of Central Cancer Registries, which included 133,808 thyroid cancer cases from the United States and Canada, to assess incidence trends among pediatric, adolescent, and young adult (PAYA) populations. METHODS: Age-adjusted incidence rates (AAIR) per 100,000 person-years (PY) were compared using rate ratios (RR), stratified by sex, age, race/ethnicity (United States only), and histology. Joinpoint regression estimated annual percentage changes (APC) and average APCs (AAPC) in AAIRs. From 1995 to 2014, thyroid cancer incidence increased by 137%. Significant increases occurred across all age groups (0-14, 15-24, 25-34, 35-39 years). The rate increase was highest for papillary thyroid cancer (AAPC = 5.50, 95% CI 5.06, 5.94), and among individuals aged 35-39 years (AAPC = 5.99, 95% CI 4.84, 7.15). Of racial/ethnic groups in the United States, non-Hispanic White individuals had the highest AAIR (6.22 per 100,000 PY). Mortality has changed minimally. CONCLUSIONS: Over the past two decades, thyroid cancer incidence has increased in individuals under 40. While evidence suggests that overdiagnosis primarily accounts for this trend, other contributing factors cannot be ruled out. Further research and surveillance of the drivers of increased incidence are critical.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".