Global assessment of cancer incidence and survival in adolescents and young adults
Bibliographic record
Abstract
In high-income countries, cancer remains the commonest cause of disease-related death in adolescents and young adults (AYAs) despite survival improvements. With more than 1,000,000 new diagnoses of cancer in AYAs annually worldwide, and their number of life-years affected by cancer being greatest of all ages, the global burden of cancer in AYAs exceeds that in all other ages. In low- and middle-income countries, where the great majority of the world's 3 billion AYAs reside, the needs of those with cancer have been identified and demand attention. Unique to the age group but universal, the psychosocial challenges they face are the utmost across life's spectrum. This lead-off article of a new series in Pediatric Blood and Cancer on AYA oncology attempts to assess the global status of this emerging discipline. The review includes the changing incidence and survival of the common cancers in AYAs-there is no other age group with a similar array of malignancies-and the specific challenges to quality and quantity of life that compromise their lives.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".