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Record W2109894318 · doi:10.1148/rg.311105099

Imaging of Cancer Predisposition Syndromes in Children

2011· review· en· W2109894318 on OpenAlexaff
Johanna Monsalve, Jeevesh Kapur, David Malkin, Paul Babyn

Bibliographic record

VenueRadiographics · 2011
Typereview
Languageen
FieldMedicine
TopicNeuroblastoma Research and Treatments
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineLi–Fraumeni syndromeNeurofibromatosisFamilial adenomatous polyposisMultiple endocrine neoplasiaCancerNeurofibromatosis type IGenetic predispositionGenetic testingColorectal cancerPathologyGermline mutationInternal medicineMutationDiseaseGeneticsGene

Abstract

fetched live from OpenAlex

The term cancer predisposition syndrome (CPS) encompasses a multitude of familial cancers in which a clear mode of inheritance can be established, although a specific gene defect has not been described in all cases. Advances in genetics and the development of new imaging techniques have led to better understanding and early detection of these syndromes and offer the potential for preclinical diagnosis of any associated tumors. As a result, imaging has become an essential component of the clinical approach to management of CPSs and the care of children suspected of having a CPS or with a confirmed diagnosis. Common CPSs in children include neurofibromatosis type 1, Beckwith-Wiedemann syndrome, multiple endocrine neoplasia, Li-Fraumeni syndrome, von Hippel-Lindau syndrome, and familial adenomatous polyposis. Radiologists should be familiar with these syndromes, their common associated tumors, the new imaging techniques that are available, and current screening and surveillance recommendations to optimize the assessment of affected children.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.822
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.362
Teacher spread0.324 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations69
Published2011
Admission routes1
Has abstractyes

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