Hereditary cancer predisposition in children: Genetic basis and clinical implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it