MétaCan
Menu
Back to cohort
Record W4283798934 · doi:10.1038/s43018-022-00403-z

Structural variants shape driver combinations and outcomes in pediatric high-grade glioma

2022· article· en· W4283798934 on OpenAlexafffund
Frank Dubois, Ofer Shapira, Noah F. Greenwald, Travis Zack, Jeremiah A. Wala, Jessica W. Tsai, Alexander Crane, Audrey Baguette, Djihad Hadjadj, Ashot S. Harutyunyan, Kiran Kumar, Mirjam Blattner-Johnson, Jayne Vogelzang, Cecília Almeida e Sousa, Kyung Shin Kang, Claire Sinai, Dayle K. Wang, Prasidda Khadka, Kathleen Lewis, Lan Nguyễn, Hayley Malkin, Patricia Ho, Ryan O’Rourke, Shu Zhang, Rose Gold, Davy Deng, Jonathan Serrano, Matija Snuderl, Chris Jones, Karen Wright, Susan Chi, Jacques Grill, Claudia L. Kleinman, Liliana Goumnerova, Nada Jabado, David Jones, Mark W. Kieran, Keith L. Ligon, Rameen Beroukhim, Pratiti Bandopadhayay

Bibliographic record

VenueNature Cancer · 2022
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsMcGill University Health CentreJewish General HospitalMcGill University
FundersNational Cancer InstituteNational Institutes of HealthFondation Gustave RoussyCancer Research UKBroad InstitutePrayers from MariaDeutsche ForschungsgemeinschaftAlex's Lemonade Stand Foundation for Childhood CancerDana-Farber/Harvard Cancer CenterInstitut Gustave-RoussySontag FoundationGilead SciencesV Foundation for Cancer ResearchDana-Farber Cancer InstituteEli Lilly and CompanyViiV HealthcareAmgenCure Starts Now FoundationCanadian Institutes of Health ResearchCompute CanadaPediatric Brain Tumor FoundationGenome Canada
KeywordsBiologyGliomaCDKN2AAmpliconReceptor tyrosine kinaseCancer researchGeneticsReceptorGene

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)0.001
metaresearch head score (Gemma)0.005
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.010
GPT teacher head0.287
Teacher spread0.278 · 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

Citations48
Published2022
Admission routes2
Has abstractno

Explore more

Same venueNature CancerSame topicGlioma Diagnosis and TreatmentFrench-language works237,207