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Record W3196488607 · doi:10.1038/s41588-021-00920-0

Genomic and evolutionary classification of lung cancer in never smokers

2021· article· en· W3196488607 on OpenAlexafffund
Tongwu Zhang, Philippe Joubert, Naser Ansari‐Pour, Zhao Wei, Phuc H. Hoang, Rachel Lokanga, Aaron L. Moye, Jennifer Rosenbaum, Abel González-Pérez, Francisco Martínez-Jiménez, Andrea Castro, Lucia Anna Muscarella, Paul L. Hofman, Dario Consonni, Angela Cecilia Pesatori, Michael Kebede, Mengying Li, Bonnie E. Gould Rothberg, Iliana Peneva, Matthew B. Schabath, Maria Luana Poeta, Manuela Costantini, Daniela Hirsch, Kerstin Heselmeyer‐Haddad, Amy Hutchinson, Mary E. Olanich, Scott M. Lawrence, Petra H. Lenz, Máire A. Duggan, Praphulla Bhawsar, Jian Sang, Jung Kim, Laura Mendoza, Natalie Saini, Leszek J. Klimczak, S. M. Ashiqul Islam, Burçak Otlu, Azhar Khandekar, Nathan Cole, Douglas R. Stewart, Jiyeon Choi, Kevin M. Brown, Neil E. Caporaso, Samuel H. Wilson, Yves Pommier, Qing Lan, Nathaniel Rothman, Jonas S. Almeida, Hannah Carter, Thomas Ried, Carla F. Kim, Núria López-Bigas, Montserrat García‐Closas, Jianxin Shi, Yohan Bossé, Bin Zhu, Dmitry A. Gordenin, Ludmil B. Alexandrov, Stephen J. Chanock, David C. Wedge, Maria Teresa Landi

Bibliographic record

VenueNature Genetics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of CalgaryUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersNational Institute of Environmental Health SciencesNational Cancer InstituteNational Heart, Lung, and Blood InstituteYale UniversityMoffitt Cancer CenterInstitut universitaire de cardiologie et de pneumologie de Québec, Université LavalConnaught FundWellcome TrustDivision of Cancer Epidemiology and Genetics, National Cancer InstituteNational Institute for Health and Care ResearchUniversité LavalDamon Runyon Cancer Research FoundationNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsBiologyLung cancerKRASGeneticsCancerGermlineGenomeSomatic cellGermline mutationGeneCancer researchBioinformaticsMutationOncologyMedicine

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)0.002
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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.006
GPT teacher head0.261
Teacher spread0.255 · 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

Citations242
Published2021
Admission routes2
Has abstractno

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