Predisposition to Pediatric and Hematologic Cancers: A Moving Target
Bibliographic record
Abstract
Our understanding of hereditary cancer syndromes in children, adolescents, and young adults continues to grow. In addition, we now recognize the wide variation in tumor spectrum found within each specific cancer predisposition syndrome including the risk for hematologic malignancies. An increased understanding of the genetic mutations, biologic consequences, tumor risk, and clinical management of these syndromes will improve patient outcome. In this article, we illustrate the diversity of molecular mechanisms by which these disorders develop in both children and adults with a focus on Li-Fraumeni syndrome, hereditary paraganglioma syndrome, DICER1 syndrome, and multiple endocrine neoplasia syndrome. This is followed by a detailed discussion of adult-onset tumors that can occur in the pediatric population including basal cell carcinoma, colorectal cancer, medullary thyroid cancer, and adrenal cortical carcinoma, and the underlying hereditary cancer syndromes that these tumors could indicate. Finally, the topic of leukemia predisposition syndromes is explored with a specific focus on the different categories of syndromes associated with leukemia risk (genetic instability/DNA repair syndromes, cell cycle/differentiation, bone marrow failure syndromes, telomere maintenance, immunodeficiency syndromes, and transcription factors/pure familial leukemia syndromes). Throughout this article, special attention is made to clinical recognition of these syndromes, genetic testing, and management with early tumor surveillance and screening.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".