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Record W2626440326 · doi:10.1016/j.ccell.2017.05.005

Intertumoral Heterogeneity within Medulloblastoma Subgroups

2017· article· en· W2626440326 on OpenAlexafffund
Florence M.G. Cavalli, Marc Remke, Ladislav Rampášek, John Peacock, David Shih, Betty Luu, Livia Garzia, Jonathon Torchia, Carolina Nör, A. Sorana Morrissy, Sameer Agnihotri, Yuan Thompson, Claudia M. Kuzan-Fischer, Hamza Farooq, Keren Isaev, Craig Daniels, Byung-Kyu Cho, Seung‐Ki Kim, Kyu‐Chang Wang, Ji Yeoun Lee, Wiesława Grajkowska, Marta Perek‐Polnik, Alexandre Vasiljevic, Cécile Faure‐Conter, Anne Jouvet, Caterina Giannini, Amulya A. Nageswara Rao, Kay Ka Wai Li, Ho‐Keung Ng, Charles G. Eberhart, Ian F. Pollack, Ronald L. Hamilton, G. Yancey Gillespie, James M. Olson, Sarah Leary, William A. Weiss, Bolesław Lach, Lola B. Chambless, Reid C. Thompson, Michael K. Cooper, Rajeev Vibhakar, Péter Hauser, Marie‐Lise C. van Veelen, Johan M. Kros, Pim J. French, Young Seob Shin, Toshihiro Kumabe, Enrique López‐Aguilar, Karel Zitterbart, Jaroslav Štěrba, Gaetano Finocchiaro, Maura Massimino, Erwin G. Van Meir, Satoru Osuka, Tomoko Shofuda, Álmos Klekner, Massimo Zollo, Joshua B. Rubin, Nada Jabado, Steffen Albrecht, Jaume Mora, Timothy Van Meter, Shin Jung, Andrew S. Moore, Andrew R. Hallahan, Jennifer A. Chan, Daniela Pretti da Cunha Tirapelli, Carlos Gilberto Carlotti, Maryam Fouladi, José Pimentel, Cláudia C. Faria, Ali G. Saad, Luca Massimi, Linda M. Liau, Helen Wheeler, Hideo Nakamura, Samer K. Elbabaa, Mario Pérezpeña-Díazconti, Fernando Chico Ponce de León, Shenandoah Robinson, Michal Zápotocký, Álvaro Lassaletta, Annie Huang, Cynthia Hawkins, Uri Tabori, Éric Bouffet, Ute Bartels, Peter B. Dirks, James T. Rutka, Gary D. Bader, Jüri Reimand, Anna Goldenberg, Vijay Ramaswamy, Michael D. Taylor

Bibliographic record

VenueCancer Cell · 2017
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalMontreal Children's HospitalMcMaster UniversityOntario Institute for Cancer ResearchUniversity of TorontoSickKids FoundationUniversity of CalgaryMcGill UniversityHamilton General HospitalHospital for Sick Children
FundersUniversity of TorontoNational Cancer InstituteNational Institutes of HealthNational Institute of Neurological Disorders and StrokeNational Institute of General Medical SciencesCancer Research SocietyTerry Fox Research InstituteMagyar Tudományos AkadémiaBrain Tumour Foundation of CanadaSwifty FoundationCanadian Institutes of Health ResearchPediatric Brain Tumor Foundation
KeywordsMedulloblastomaBiologyPTCH1Sonic hedgehogComputational biologyHomogeneousDNA methylationGenomeGeneticsBioinformaticsCancer researchGeneGene expression

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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.315
Teacher spread0.284 · 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

Citations1,255
Published2017
Admission routes2
Has abstractno

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