Time trends of cancer incidence in young adults (20-49 years) in Italy. A population - based study, 2008-2017
Bibliographic record
Abstract
OBJECTIVE: To evaluate short-term (2008-2017) cancer incidence trends in Italy for individuals aged 20-49 years by sex and cancer type. METHODS: Observational study from population-based data collected by 20 Italian Cancer Registries, covering 33% of the Italian population. The age-standardized incidence rates (ASRs), overall and stratified by area, sex, cancer site or type, and major age groups (i.e., 20-39, 40-49), were computed. RESULTS: In 2008-2017, cancer incidence rates were almost two times higher in Italian women aged 20-49 than in age-corresponding men (202.2 vs 112.4 per 100,000) on account of elevated rates of breast and thyroid cancers. Contrasting trends emerged according to cancer sites/types. ASRs for female breast cancer increased steadily from 2008 (82.4) to 2014 (86.2) and remained unchanged thereafter (i.e., 86.5 in 2017). During the study period, there was an increase for testicular cancer, skin melanoma in both sexes, and thyroid cancer until 2013 (followed by a slight decrease from 2014 to 2017). Conversely, ASRs consistently declined for colorectal cancer and were substantially stable or slightly decreasing for cervix uteri (from 8.1 to 7.7), ovary (from 7.5 to 6.9) and non-Hodgkin lymphoma (from 8.3 to 7.6 in men and from 5.9 to 5.5 in women). CONCLUSIONS: Study findings do not support a unique temporal pattern for the incidence of early-onset cancer in Italy until 2017, as reported in other countries. Increases in incidence documented in both sexes for some tumor sites was counterbalanced by a decrease in other sites. The importance of supporting prevention strategies from the youngest of ages must be emphasized, and the role of anticipated screening should be carefully addressed.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".