An estimate of the number of people in Italy living after a childhood cancer
Bibliographic record
Abstract
Cancers diagnosed in children below the age of 15 years represent 1.2% of all cancer cases, and survival after a childhood cancer has greatly improved over the past 40 years in all high income countries. This study aims to estimate the number of people living in Italy after a childhood cancer for all cancers combined and for a selection of cancer types. We computed 15-year prevalence using data from 15 Italian population-based cancer registries (covering 19% of Italian population) and estimated complete prevalence for Italy by using the CHILDPREV method, implemented in the COMPREV software. A total of 44,135 persons were alive at January 1st, 2010 after a cancer diagnosed during childhood. This number corresponds to a proportion of 73 per 100,000 Italians and to about 2% of all prevalent cases. Among them, 54% were males and 64% had survived after being diagnosed before 1995, the start of the observation period. A quarter of all childhood prevalent cases were diagnosed with brain and central nervous system tumors, a quarter with acute lymphoid leukemia, and 7% with Hodgkin lymphoma. Nearly a quarter of prevalent patients were aged 40 years and older. Information about the number of people living after a childhood cancer in Italy by cancer type and their specific health care needs may be helpful to health-care planners and clinicians in the development of guidelines aimed to reduce the burden of late effect of treatments during childhood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 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) | 0.003 | 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".