Cancer incidence and mortality trends in Australian adolescents and young adults, 1982–2007
Bibliographic record
Abstract
BACKGROUND: Increasing incidence and lack of survival improvement in adolescents and young adults (AYAs) with cancer have led to increased awareness of the cancer burden in this population. The objective of this study was to describe overall and type-specific cancer incidence and mortality trends among AYAs in Western Australia from 1982-2007. METHODS: Age-adjusted incidence and mortality rates were calculated for all malignancies combined and for each of the most common diagnostic groups, using five-year age-specific rates. Joinpoint regression analysis was used to derive annual percentage changes (APC) for incidence and mortality rates. RESULTS: The annual incidence rate for all cancers combined increased in males from 1982 until 2000 (APC = 1.5%, 95%CI: 0.9%; 2.1%) and then plateaued, whilst rates for females remained stable across the study period (APC = -0.1%; 95%CI: -0.2%; 0.4%) across the study period. For males, significant incidence rate increases were observed for germ cell tumors, lymphoblastic leukemia and thyroid cancer. In females, the incidence of Hodgkin's lymphoma, colorectal and breast cancers increased. Significant incidence rate reductions were noted for cervical, central nervous system and lung cancers. Mortality rates for all cancers combined decreased from 1982 to 2005 for both males (APC = -2.6%, 95%CI:-3.3%;-2.0%) and females (APC = -4.6%, 95%CI:-5.1%;-4.1%). With the exception of bone sarcoma and lung cancer in females, mortality rates for specific cancer types decreased significantly for both sexes during the study period. CONCLUSIONS: Incidence of certain AYA cancers increased, whilst it decreased for others. Mortality rates decreased for most cancers, with the largest improvement observed for breast carcinomas. Further research is needed to identify the reasons for the increasing incidence of certain cancers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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, 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".