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Record W2900771557 · doi:10.1038/s41586-018-0703-0

Sensitive tumour detection and classification using plasma cell-free DNA methylomes

2018· article· en· W2900771557 on OpenAlexafffund
Shu Yi Shen, Rajat Singhania, Gordon Fehringer, Ankur Chakravarthy, Michael H. A. Roehrl, Dianne Chadwick, Philip C. Zuzarte, Ayelet Borgida, Ting Ting Wang, Tiantian Li, Olena Kis, Zhen Zhao, Anna Spreafico, Tiago da Silva Medina, David Roulois, Ilias Ettayebi, Zhuo Chen, Signy Chow, Tracy Murphy, Andrea Arruda, Grainne M. O’Kane, Jessica Liu, Mark Mansour, John D. McPherson, Catherine O′Brien, Natasha B. Leighl, Philippe L. Bédard, Neil Fleshner, Geoffrey Liu, Mark D. Minden, Steven Gallinger, Anna Goldenberg, Trevor J. Pugh, Michael M. Hoffman, Scott V. Bratman, Daniel D. De Carvalho

Bibliographic record

VenueNature · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPublic Health OntarioOntario Institute for Cancer ResearchToronto General HospitalUniversity Health NetworkUniversity of TorontoMount Sinai HospitalLunenfeld-Tanenbaum Research InstituteSinai Health SystemPrincess Margaret Cancer Centre
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchUniversity of TorontoPrincess Margaret Cancer FoundationCancer Care Ontario
KeywordsDNA methylationCell-free fetal DNAEpigeneticsMethylated DNA immunoprecipitationBiologyDNACancerStage (stratigraphy)Somatic cellComputational biologyCancer detectionCancer researchDNA extractionGeneGeneticsPolymerase chain reactionGene 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)0.001
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.250
Teacher spread0.242 · 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

Citations1,025
Published2018
Admission routes2
Has abstractno

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