Rare cancers in childhood and adolescence in Brazil: First report of data from 19 population‐based cancer registries
Bibliographic record
Abstract
BACKGROUND: Rare childhood cancer is challenging to define. The Italian Pediatric Rare Tumor (TREP) Study considers rare tumors to include solid malignancies characterized by an annual incidence rate of <2 cases per 1 million and not enrolled in clinical trials. The objective of the current study was to analyze the population incidence rate of rare tumors among children and adolescents (those aged birth-19 years) in Brazil. METHODS: Incidence data were obtained from 19 population-based cancer registries covering the 5 geographic regions in Brazil. Newly diagnosed cases were selected according to the TREP definition, using the International Classification of Diseases for Oncology. To calculate the crude incidence rate, the numbers of incident children and adolescents with a specific rare cancer were divided by the corresponding person-years lived for the population aged <20 years during the same period. RESULTS: Two tumors had an incidence rate that was >2 cases per 1 million (thyroid and skin cancers) in adolescents only. Several tumors demonstrated variations in incidence across the Brazilian regions. Adrenocortical carcinoma had a high incidence rate (4 cases per 1 million) in the south region among children aged <10 years. Thyroid and skin carcinoma had higher incidence rates in the midwest, southeast, and south regions. CONCLUSIONS: Due to the extraordinary rarity of these events, networking is important for improving basic research, clinical studies, and trials. Centralization of diagnosis is the only way to improve the diagnosis and treatment of children affected by these rare diseases. The registration and surveillance of rare pediatric cancers are crucial from a public health point of view, and therefore the quality of registration has to be improved.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".