Incidence and incidence trends of the most frequent cancers in adolescent and young adult Americans, including “nonmalignant/noninvasive” tumors
Bibliographic record
Abstract
BACKGROUND: Incidence rates and trends of cancers in adolescents and young adults (AYAs) ages 15 to 39 years were reexamined a decade after the US National Cancer Institute AYA Oncology Progress Review Group was established. METHODS: Data from the Surveillance, Epidemiology, and End Results program through 2011 were used to ascertain incidence trends since the year 2000 of the 40 most frequent cancers in AYAs, including tumors with nonmalignant/noninvasive behavior. RESULTS: Seven cancers in AYAs exhibited an overall increase in incidence; in 4, the annual percent change (APC) exceeded 3 (kidney, thyroid, uterus [corpus], and prostate cancer); whereas, in 3, the APC was between 0.7 and 1.4 (acute lymphoblastic leukemia and cancers of the colorectum and testis). Eight cancers exhibited statistically significant decreases in incidence among AYAs: Kaposi sarcoma (KS), fibromatous neoplasms, melanoma, and cancers of the anorectum, bladder, uterine cervix, esophagus, and lung, each with an APC less than -1. AYAs had a higher proportion of noninvasive tumors than either older or younger patients. CONCLUSIONS: An examination of cancer incidence patterns in AYAs observed over the recent decade reveal a complex pattern. Thyroid cancer by itself accounts for most of the overall increase and is likely caused by overdiagnosis. Reductions in cervix and lung cancer, melanoma, and KS can be attributed to successful national prevention programs. A higher proportion of noninvasive tumors in AYAs than in children and older adults indicates a need to revise the current system of classifying tumors in this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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, 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".