Trends in the Incidence of Cancer Among Adolescent and Young Adults in Alberta, 1983–2017: A Population-Based Study Using Cancer Registry Data
Bibliographic record
Abstract
Purpose: To describe the cancer incidence burden and trends among adolescent and young adults (AYAs) in Alberta, Canada over a 35-year period. Methods: We obtained data from the Alberta Cancer Registry on all first primary cancers, excluding non-melanoma skin cancer, diagnosed at ages 15–39 years among residents in Alberta from 1983 to 2017. Cancers were classified by using Barr's AYA cancer classification system. Age-standardized incidence rates (ASIR) and the average annual percentage change (AAPC) in incidence rates were calculated. Statistically significant changes in the AAPC during the study period were assessed using Joinpoint regression. Results: Overall, 23,652 incident cases of AYA cancer were diagnosed in Alberta. Females accounted for ∼60% of the diagnoses. AYA cancer increased significantly over the study period overall (AAPC: 0.5%; 95%CI: 0.3%–0.7%), for each sex (AAPC male : 0.7%; 95%CI: 0.4%–0.9%; AAPC female : 0.4%; 95%CI: 0.2%–0.6%), and among male and female 20–39 year-olds. Although statistically significant increases were observed in 11 out of 29 cancer sites for at least a portion of the study period, with significant AAPCs ranging from 0.8% (95%CI: 0.01%–1.5%) to 6.6% (95%CI: 4.6%–8.5%), the main driver was thyroid cancer (AAPC: 3.7%; 95%CI: 3.2%–4.2%). Statistically significant decreases were observed for six cancer sites, with AAPCs ranging from −6.4% (95%CI: −8.7% to −4.1%) to −1.1% (95%CI: −1.8% to −0.5%). Conclusions: There is a growing cancer burden among AYAs in Alberta, which is driven primarily by thyroid cancer and early-onset cancers in males. These results highlight the need for etiological studies and tertiary strategies to prevent and mitigate morbidity and mortality in the AYA population.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".