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Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer
A. Gordon Robertson, Jaegil Kim, Hikmat Al‐Ahmadie, Joaquim Bellmunt, Guangwu Guo, Andrew D. Cherniack, Toshinori Hinoue, Peter W. Laird, Katherine A. Hoadley, Rehan Akbani, Mauro A. A. Castro, Ewan A. Gibb, Rupa S. Kanchi, Dmitry A. Gordenin, Sachet A. Shukla, Francisco Sánchez-Vega, Donna E. Hansel, Bogdan Czerniak, Victor E. Reuter, Xiaoping Su, Benílton de Sá Carvalho, Vinicius S Chagas, Karen Mungall, Sara Sadeghi, Chandra Sekhar Pedamallu, Yiling Lu, Leszek J. Klimczak, Jiexin Zhang, Caleb Choo, Akinyemi I. Ojesina, Susan Bullman, Kristen Leraas, Tara M. Lichtenberg, Catherine J. Wu, N. Schultz, Gad Getz, Matthew Meyerson, Gordon B. Mills, David J. McConkey, Monique Albert, Iakovina Alexopoulou, Adrian Ally, Tatjana Antic, Manju Aron, Miruna Balasundaram, John M.S. Bartlett, Stephen B. Baylin, Allison Beaver, İnanç Birol, Lori Boice, Jay Bowen, Reanne Bowlby, Denise Brooks, Bradley M. Broom, Wiam Bshara, Eric Burks, Flavio Mavignier Cárcano, Rebecca Carlsen, Benilton S. Carvalho, André Lopes Carvalho, Eric Castle, James W.F. Catto, David Chesla, Eric Chuah, Sudha Chudamani, Victoria K. Cortessis, Sandra Cottingham, Daniel Crain, Erin Curley, Siamak Daneshmand, John A. Demchok, Noreen Dhalla, Hooman Djaladat, John Eckman, Sophie Egea, Jay Engel, Ina Felau, Martin L. Ferguson, Johanna Gardner, Julie M. Gastier‐Foster, Mark Gerken, Carmen Gomez‐Fernandez, Jodi Harr, Arndt Hartmann, Lynn M. Herbert, Thai H. Ho, Robert A. Holt, Carolyn M. Hutter, Steven J.M. Jones, Mercè Jordà, Richard J. Kahnoski, Phillip H. Lai, Brian R. Lane, Seth P. Lerner, Jia Liu, Laxmi Lolla, Yair Lotan, Fabiano R. Lucchesi, Yussanne Ma, Roberto Días Machado, Dennis T. Maglinte, David Mallery, Marco A. Marra, Sue Martin, Michael Mayo, Anoop Meraney, Alireza Moinzadeh, Richard A. Moore, Edna M. Mora Pinero, Scott Morris, Quaid Morris, Karen Mungall, Andrew J. Mungall, Jerome Myers, Rashi Naresh, Peter H. O’Donnell, Dipen J. Parekh, Jeremy Parfitt, Joseph Paulauskis, Robert Penny, Todd Pihl, Sima P. Porten, Mario Quintero-Aguilo, Nilsa C. Ramirez, W. Kimryn Rathmell, Kimberly Rieger‐Christ, Charles Saller, Andrew Salner, George E. Sandusky, Cristovam Scapulatempo‐Neto, Jacqueline E. Schein, Anne Schuckman, Nikolaus Schultz, Candace Shelton, Troy Shelton, Jeff Simko, Parminder Singh, Payal Sipahimalani, Norm D. Smith, Heidi J. Sofia, Andrea Sorcini, Melissa Stanton, Gary D. Steinberg, Robert Stoehr, Travis Sullivan, Qiang Sun, Angela Tam, Roy Tarnuzzer, Katherine Tarvin, Helge Täubert, Nina Thiessen, Leigh B. Thorne, Kane Tse, Kelinda Tucker, David Van Den Berg, Kim E. van Kessel, Sven Wach, Yunhu Wan, Zhining Wang, John N. Weinstein, Daniel J. Weisenberger, Lisa Wise, Tina Wong, Ye Wu, Liming Yang, Leigh Anne Zach, Jean C. Zenklusen, Jiashan Zhang, Erik Zmuda, Ellen C. Zwarthoff
Bibliographic record
VenueCell · 2017
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsCanada's Michael Smith Genome Sciences CentreBC Cancer Agency
FundersNational Cancer InstituteUniversidade Estadual de CampinasPró-Reitoria de Pesquisa, Universidade Federal do Rio Grande do SulConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorBaylor College of MedicineLeukemia and Lymphoma SocietyU.S. Department of DefenseNational Center for Advancing Translational SciencesNational Human Genome Research InstituteMichael and Susan Dell FoundationAstraZenecaFundo de Apoio ao Ensino, à Pesquisa e Extensão, Universidade Estadual de CampinasMary K. Chapman FoundationNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsBiologyBladder cancerCancerComputational biologyCancer researchGenetics
Abstract
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 imitationNot 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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 head0.025
GPT teacher head0.293
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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 designBench or experimental
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
Citations2,495
Published2017
Admission routes1
Has abstractno