Hereditary cancer predisposition in children: Genetic basis and clinical implications
Bibliographic record
Abstract
Although cancer predisposition syndromes are rare and malignancies arising in this context account for only 1-10% of childhood tumors, studies performed in affected patients and their families have been of unique value for the understanding of cancer development. Three classes of genes (tumor suppressor genes, oncogenes and stability genes) have been identified and shown to be involved in the pathogenesis of familial, as well as sporadic tumors. Cancer has long been recognized as a genetic disease of somatic cells. Despite improved understanding of the molecular basis of predisposition to cancer and better diagnostic tools, the care of these patients and their families remains a major challenge for the clinician. Medical, psychological, ethical and legal issues have to be considered. This review focuses on examples of each class of inherited cancer predisposition syndromes with special implications for patients in the pediatric age group, including retinoblastoma predisposition, Li-Fraumeni syndrome, multiple endocrine neoplasia disorders and Fanconi anemia. The genetic basis of cancer predisposition is discussed as well as the major concepts and controversies in the clinical management of these patients and their families.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".