Bibliographic record
Abstract
Abstract Pediatric cancers include a broad spectrum of diseases. Cancer is diagnosed in only 8400 children in the United States less than 15 years of age annually, although death from cancer is the second major cause of mortality in developed countries. Childhood cancers differ from adult cancers in their origins and histologic subtypes, their etiologies, their response to treatment, and the outcomes. In the adult population, epithelial cancers are most common and many are related to environmental carcinogens. In contrast, pediatric malignancies more commonly arise in hematopoietic tissue or in the central nervous system (CNS). The most common pediatric malignancies are acute leukemia, non‐Hodgkin's lymphoma, Hodgkin's disease, and primary CNS tumors. Neuroblastoma, Wilms' tumor, rhabdomyosarcoma, and retinoblastomas are the most common solid tumors occurring in children. The etiology of most childhood malignancies is unknown, although some solid tumors do occur in association with recognized genetic defects. Bilateral retinoblastoma, for instance, occurs in children with mutations in the retinoblastoma tumor suppressor gene RB1 . Wilms' tumor occurs in association with mutations in the WT1 gene in the Denys‐Drash Syndrome. Rhabdomyosarcomas are seen in children with the LiFraumeni syndrome with p53 gene mutations. Major advances in cancer genetics and the molecular biology of cancer have been gained through research in pediatric malignancies. Despite its rarity in comparison to adult malignancies, many of the most important discoveries about cancer biology and cancer genetics have come from research in pediatric cancers. In addition, the role of prognostic factors in determining treatment for an individual patient is best exemplified by neuroblastoma which is discussed in this chapter. At the present time, only a limited number of cancers in children have prognostic factors that have been prospectively evaluated and confirmed. In this chapter, we review those pediatric cancers that have clearly defined prognostic factors. The understanding of cancer genetics gained from the study of pediatric cancers, the identification of prognostic factors, their confirmation in clinical trials, and their widespread acceptance can be viewed as a paradigm for prognostic factors in cancer patient management.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.158 | 0.079 |
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".