Genetic predisposition to cancers in children and adolescents
Bibliographic record
Abstract
PURPOSE OF REVIEW: Childhood cancer is rare, but it remains the leading cause of disease-related mortality among children 1-14 years of age. As exposure to environmental factors is lower in children, inherited genetic factors become an important player in the cause of childhood cancer. This review highlights the current knowledge and approach for cancer predisposition syndromes in children. RECENT FINDINGS: Current literature suggests that 10-18% of paediatric cancer patients have an underlying genetic susceptibility to their disease. With better knowledge and technology, more genes and syndromes are being discovered, allowing tailored treatment and surveillance for the probands and their families.Studies have demonstrated that focused surveillance can detect early malignancies and increase overall survival in several cancer predisposition syndromes. Various approaches have been proposed to refine early tumour detection strategies while minimizing the burden on patients and families. Newer therapeutic strategies are being investigated to treat, or even prevent, tumours in children with cancer predisposition. SUMMARY: This review summarizes the current knowledge about different cancer predisposition syndromes, focusing on the diagnosis, genetic counselling, surveillance and future directions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".