Sociodemographic characteristics associated with thyroid cancer risk in Canada.
Bibliographic record
Abstract
BACKGROUND: Thyroid cancer incidence in Canada has increased rapidly over the past 25 years. This study examines thyroid cancer incidence and relative risk according to individual-level sociodemographic characteristics in two population-based cohorts. DATA AND METHODS: The analysis uses data from the 1991 and 2001 Canadian Census Health and Environment Cohorts (CanCHECs). Using nine years of cancer follow-up for both time periods, age-standardized incidence rates of thyroid cancer were estimated by sex-with sex-specific estimates produced by immigrant status, ethnicity, educational attainment and family income-and by histology type. All characteristics were included in sex-specific standard Cox proportional hazard models to examine the relative risk of thyroid cancer and the relative risk of papillary versus non-papillary thyroid cancer. RESULTS: A significant increase over time in thyroid cancer incidence was observed for both sexes, and across all characteristics. Immigrant status and ethnicity were each independently associated with the risk of thyroid cancer, with immigrant men and women and East and Southeast Asian women at higher risk. Men and women who had a postsecondary diploma or higher or who were in the highest income quintile were at increased risk of being diagnosed with papillary thyroid cancer, but not with non-papillary thyroid cancer. DISCUSSION: While increased detection has played a role in the rising incidence of thyroid cancer in Canada, it does not fully account for the greater relative risk among the immigrant population and certain ethnic groups. More research is needed to better understand the determinants of the increased risk in these populations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".