Cancer burden in adolescents and young adults in Europe
Bibliographic record
Abstract
BACKGROUND: Cancer epidemiology is unique in adolescents and young adults (AYAs; aged 15-39 years). The European Society for Medical Oncology/European Society for Paediatric Oncology (ESMO/SIOPE) AYA Working Group aims to describe the burden of cancers in AYAs in Europe and across European Union (EU) countries. PATIENTS AND METHODS: We used data available on the Global Cancer Observatory. We retrieved crude and age-standardised (World Standard Population) incidence and mortality rates. We reported about AYA cancer burden in Europe and between 28 EU member states. We described incidence and mortality for all cancers and for the 13 cancers most relevant to the AYA population. RESULTS: Incidence and mortality varied widely between countries with the highest mortality observed in Eastern EU countries. Cancers of the female breast, thyroid and male testis were the most common cancers across countries followed by melanoma of skin and cancers of the cervix. Variations in cancer incidence rates across different populations may reflect different distribution of risk factors, variations in the implementation or uptake of screening as well as overdiagnosis. AYA cancer mortality disparities may be due to variation in early-stage diagnoses, different public education and awareness of cancer symptoms, different degrees of access or availability of treatment. CONCLUSIONS: Our results highlight the future health care needs and requirements for AYA-specialised services to ensure a homogeneous treatment across different countries as well as the urgency for preventive initiatives that can mitigate the increasing burden.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".