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Record W2093297340 · doi:10.1038/ng.3202

Combined hereditary and somatic mutations of replication error repair genes result in rapid onset of ultra-hypermutated cancers

2015· article· en· W2093297340 on OpenAlexafffund
Adam Shlien, Brittany Campbell, Richard de Borja, Ludmil B. Alexandrov, Daniele Merico, David C. Wedge, Peter Van Loo, Patrick Tarpey, Paul Coupland, Sam Behjati, Aaron Pollett, Tatiana Lipman, Abolfazl Heidari, Shriya Deshmukh, N. Avitzur, Bettina Meier, Moritz Gerstung, Ye Hong, Diana M. Merino, Manasa Ramakrishna, Marc Remke, Roland Arnold, Gagan B. Panigrahi, Neha Thakkar, Karl P. Hodel, Erin E. Henninger, A. Yasemin Göksenin, Doua Bakry, George S. Charames, Harriet Druker, Jordan Lerner‐Ellis, Matthew Mistry, Rina Dvir, Ronald Grant, Ronit Elhasid, Roula Farah, Glenn Taylor, Paul C. Nathan, Sarah Alexander, Shay Ben‐Shachar, Simon C. Ling, Steven Gallinger, Shlomi Constantini, Peter B. Dirks, Annie Huang, Stephen W. Scherer, Richard G. Grundy, Carol Durno, Melyssa Aronson, Anton Gartner, M. Stephen Meyn, Michael D. Taylor, Zachary F. Pursell, Christopher E. Pearson, David Malkin, P. Andrew Futreal, Michael R. Stratton, Éric Bouffet, Cynthia Hawkins, Peter J. Campbell, Uri Tabori

Bibliographic record

VenueNature Genetics · 2015
Typearticle
Languageen
FieldMedicine
TopicGenetic factors in colorectal cancer
Canadian institutionsOntario Institute for Cancer ResearchUniversity of TorontoSickKids FoundationMount Sinai HospitalHospital for Sick Children
FundersCanadian Institutes of Health ResearchCancer Research UKWellcome Trust
KeywordsBiologySomatic cellGeneticsGeneReplication (statistics)MutationVirology

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentalhigh
models splitAgreement compares identical category sets and study designs across arms.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

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 communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.311
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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Bench 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

Citations381
Published2015
Admission routes2
Has abstractno

Explore more

Same venueNature GeneticsSame topicGenetic factors in colorectal cancerFrench-language works237,207