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Record W4410172813 · doi:10.1038/s43018-025-00962-x

A population-based analysis of the molecular landscape of glioma in adolescents and young adults reveals insights into gliomagenesis

2025· article· en· W4410172813 on OpenAlexafffund
Julie Bennett, Adrian Levine, Liana Nobre, Logine Negm, Jiil Chung, Karen Fang, Monique Johnson, Martin Komosa, Stacey Krumholtz, Nuno M. Nunes, Mansuba Rana, Scott Ryall, Javal Sheth, Robert Siddaway, Tejus Bale, Éric Bouffet, Michael D. Cusimano, Sunit Das, Jay Detsky, Peter B. Dirks, Matthias A. Karajannis, Paul Kongkham, Alexandra Giantini-Larsen, Bryan Kincheon Li, Mary Jane Lim-Fat, Andrew Lin, Warren Mason, Alexandra Miller, James Perry, Arjun Sahgal, Sameer Farouk Sait, Derek S. Tsang, Gelareh Zadeh, Normand Laperrière, Lananh Nguyen, Andrew Gao, Julia Keith, David G. Muñoz, Uri Tabori, Cynthia Hawkins

Bibliographic record

VenueNature Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsToronto Public HealthHealth Sciences CentreSunnybrook Health Science CentreUniversity Health NetworkUniversity of TorontoSickKids FoundationPublic Health OntarioStollery Children's HospitalHospital for Sick ChildrenToronto Western HospitalPrincess Margaret Cancer Centre
FundersNational Cancer InstituteCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchGovernment of CanadaU.S. Department of Health and Human Services
KeywordsGliomaPopulationGeographyMedicineCancer researchEnvironmental health

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)0.001
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.269
Teacher spread0.265 · 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

Citations14
Published2025
Admission routes2
Has abstractno

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