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Record W2260491557 · doi:10.1038/nature16478

Divergent clonal selection dominates medulloblastoma at recurrence

2016· article· en· W2260491557 on OpenAlexafffund
A. Sorana Morrissy, Livia Garzia, David Shih, Scott Zuyderduyn, Xi Huang, Patryk Skowron, Marc Remke, Florence M.G. Cavalli, Vijay Ramaswamy, Patricia Lindsay, Salomeh Jelveh, Laura Donovan, Xin Wang, Betty Luu, Kory Zayne, Yisu Li, Chelsea Mayoh, Nina Thiessen, Eloi Mercier, Karen Mungall, Yusanne Ma, Kane Tse, Thomas Zeng, Karey Shumansky, Andrew Roth, Sohrab P. Shah, Hamza Farooq, Noriyuki Kijima, Borja Holgado, John J. Y. Lee, Stuart Matan-Lithwick, Jessica Liu, Stephen C. Mack, Alex Manno, Kulandaimanuvel Antony Michealraj, Carolina Nör, John Peacock, Lei Qin, Jüri Reimand, Adi Rolider, Yuan Thompson, Xiaochong Wu, Trevor J. Pugh, Adrian Ally, Mikhail Bilenky, Yaron S.N. Butterfield, Rebecca Carlsen, Young Cheng, Eric Chuah, Richard Corbett, Noreen Dhalla, Anyuan He, Darlene Lee, Haiyan I. Li, William D. Long, Michael Mayo, Patrick Plettner, Jenny Q. Qian, Jacqueline E. Schein, Angela Tam, Tina Wong, İnanç Birol, Yongjun Zhao, Cláudia C. Faria, José Pimentel, Sofía Nunes, Tarek Shalaby, Michael A. Grotzer, Ian F. Pollack, Ronald L. Hamilton, Xiao‐Nan Li, Anne Bendel, Daniel W. Fults, Andrew W. Walter, Toshihiro Kumabe, Teiji Tominaga, V. Peter Collins, Yoon-Jae Cho, Caitlin Hoffman, David Lyden, Jeffrey H. Wisoff, James H. Garvin, Duncan Stearns, Luca Massimi, Ulrich Schüller, Jaroslav Štěrba, Karel Zitterbart, Stéphanie Puget, Olivier Ayrault, Sandra E. Dunn, Daniela Pretti da Cunha Tirapelli, Carlos Gilberto Carlotti, Helen Wheeler, Andrew R. Hallahan, Wendy J. Ingram, Tobey J. MacDonald, Jeffrey J. Olson, Erwin G. Van Meir, Ji-Yeoun Lee, Kyu‐Chang Wang, Seung‐Ki Kim, Byung-Kyu Cho, Torsten Pietsch, Gudrun Fleischhack, Stephan Tippelt, Young Seob Shin, Simon Bailey, Janet C. Lindsey, Steven C. Clifford, Charles G. Eberhart, Michael K. Cooper, Roger J. Packer, Maura Massimino, Maria Luisa Garrè, Ute Bartels, Uri Tabori, Cynthia Hawkins, Peter B. Dirks, Éric Bouffet, James T. Rutka, Robert J. Wechsler‐Reya, William A. Weiss, Lara S. Collier, Adam J. Dupuy, Andrey Korshunov, David Jones, Marcel Kool, Paul A. Northcott, Stefan M. Pfister, David A. Largaespada, Andrew J. Mungall, Richard A. Moore, Nada Jabado, Gary D. Bader, Steven J.M. Jones, David Malkin, Marco A. Marra, Michael D. Taylor

Bibliographic record

VenueNature · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsSimon Fraser UniversityMcGill UniversityCanada's Michael Smith Genome Sciences CentreBC Children's HospitalUniversity Health NetworkUniversity of TorontoSickKids FoundationBC Cancer AgencyLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalPrincess Margaret Cancer CentreUniversity of British ColumbiaHospital for Sick Children
FundersNational Institute of Neurological Disorders and StrokeCancer Research UKNational Cancer InstituteSparksCanadian Institutes of Health ResearchBrain Tumour Charity
KeywordsMedulloblastomaclone (Java method)Radiation therapyTargeted therapyBiologyCancerMedicineOncologyCancer researchInternal medicineGeneticsGene

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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.004
GPT teacher head0.232
Teacher spread0.228 · 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

Citations343
Published2016
Admission routes2
Has abstractno

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