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Record W2132546867 · doi:10.1038/ng.2529

The genetic landscape of high-risk neuroblastoma

2013· article· en· W2132546867 on OpenAlexafffund
Trevor J. Pugh, Olena Morozova, Edward F. Attiyeh, Shahab Asgharzadeh, Jun S. Wei, Daniel Auclair, Scott L. Carter, Kristian Cibulskis, Megan Hanna, Adam Kieżun, Jaegil Kim, Michael S. Lawrence, Lee Lichenstein, Aaron McKenna, Chandra Sekhar Pedamallu, Alex H. Ramos, Erica Shefler, Andrey Sivachenko, Carrie Sougnez, Chip Stewart, Adrian Ally, İnanç Birol, Readman Chiu, Richard Corbett, Martin Hirst, Shaun D. Jackman, Baljit Kamoh, Alireza Hadj Khodabakshi, Martin Krzywinski, Allan Lo, Richard A. Moore, Karen Mungall, Jenny Q. Qian, Angela Tam, Nina Thiessen, Yongjun Zhao, Kristina A. Cole, Maura Diamond, Sharon J. Diskin, Yaël P. Mossé, Andrew Wood, Lingyun Ji, Richard Sposto, Thomas Badgett, Wendy B. London, Yvonne Moyer, Julie M. Gastier‐Foster, Malcolm A. Smith, Jaime M. Guidry Auvil, Daniela S. Gerhard, Michael D. Hogarty, Steven J.M. Jones, Eric S. Lander, Stacey Gabriel, Gad Getz, Robert C. Seeger, Javed Khan, Marco A. Marra, Matthew Meyerson, John M. Maris

Bibliographic record

VenueNature Genetics · 2013
Typearticle
Languageen
FieldMedicine
TopicNeuroblastoma Research and Treatments
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersNational Cancer InstituteNational Human Genome Research InstituteNational Institute on Minority Health and Health DisparitiesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsATRXBiologyNeuroblastoma RAS viral oncogene homologPTPN11NeuroblastomaGermline mutationGermlineExome sequencingCHEK2GeneticsSomatic cellCancer researchMutationExomeGeneKRAS

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.249
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations1,219
Published2013
Admission routes2
Has abstractno

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