Thyroid cancer incidence in Canada: a national cancer registry analysis
Bibliographic record
Abstract
BACKGROUND: Thyroid cancer incidence rates are increasing in many developed countries while mortality rates remain stable. International evidence shows that the increase in incidence rates is mostly caused by overdiagnosis of small papillary cancers. We sought to describe how thyroid cancer incidence has changed and how it varies between provinces in Canada. METHODS: Data were obtained from the National Cancer Incidence Reporting System, causes of death tables and the Canadian Cancer Registry using the 1991 census population structure. We report thyroid cancer incidence by sex, age and province and mortality by sex from 1970 to 2012. RESULTS: Since 1970, age-standardized thyroid cancer incidence rates have increased in women from 3.9 to 23.4 per 100 000 and in men from 1.5 to 7.2 per 100 000 while mortality rates have remained stable at around 0.5 per 100 000 for both sexes. In 2012, incidence rates for both women and men were highest in Ontario (31.5 and 9.2 per 100 000, respectively) and lowest in British Columbia (13.2 and 4.5 per 100 000, respectively). Age-specific incidence rates were the highest in Ontarian women aged 50-54 years, at 65.2 per 100 000. INTERPRETATION: The rapid increase in thyroid cancer incidence especially since 1990, the variation among provinces and the peak in middle-aged women does not correspond to any known cause or risk factor for disease, although the lack of change in mortality rates suggests that serious thyroid cancer has not increased. The likely cause of the increase in incidence is an overdiagnosis epidemic for clinically unimportant lesions detected by modern diagnostic imaging. To reduce the harms of overtreatment, overdiagnosis should be reduced, through more judicious use of diagnostic imaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".