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Record W4232900213 · doi:10.1002/0471463736.tnmp46

Pediatric Cancers

2003· other· en· W4232900213 on OpenAlexaff
Karen J. Marcus, David Hodgson

Bibliographic record

VenueTNM Online · 2003
Typeother
Languageen
FieldMedicine
TopicNeuroblastoma Research and Treatments
Canadian institutionsInstitute for Clinical Evaluative SciencesPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsRetinoblastomaMedicineCancerPediatric cancerRhabdomyosarcomaWilms' tumorDiseaseEtiologyNeuroblastomaOncologyLymphomaPopulationLeukemiaSarcomaPathologyInternal medicineBiologyGeneGenetics

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.032
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0330.001

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.

Opus teacher head0.021
GPT teacher head0.331
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2003
Admission routes1
Has abstractyes

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