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Record W3178692152 · doi:10.1016/j.jtho.2021.06.017

Liquid Biopsy for Advanced NSCLC: A Consensus Statement From the International Association for the Study of Lung Cancer

2021· review· en· W3178692152 on OpenAlexaff
Christian Rolfo, Philip C. Mack, Giorgio V. Scagliotti, Charu Aggarwal, Maria E. Arcila, Fabrice Barlési, Trever G. Bivona, Maximilian Diehn, Caroline Dive, Rafał Dziadziuszko, Natasha B. Leighl, Umberto Malapelle, Tony Mok, Nir Peled, Luis E. Raez, Lecia V. Sequist, Lynette M. Sholl, Charles Swanton, Chris Abbosh, Daniel S.W. Tan, Heather A. Wakelee, Ignacio I. Wistuba, Rebecca A. Bunn, Janet Freeman‐Daily, Murry W. Wynes, Chandra P. Belani, Tetsuya Mitsudomi, David R. Gandara

Bibliographic record

VenueJournal of Thoracic Oncology · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersJanssen PharmaceuticalsStand Up To CancerEMD SeronoGenentechEuropean Society for Medical OncologyEisaiDaiichi-SankyoEuropean School of OncologyNational Institutes of HealthRosetrees TrustCancer Research UKANGLEMirati TherapeuticsBayer FundClovis OncologyAstex PharmaceuticalsMeso Scale DiagnosticsLes Laboratories Pierre FabreMerck Sharp and DohmeTakeda Pharmaceuticals U.S.A.Intel CorporationExelixisACEA BiosciencesNovartisCelgeneNational Cancer InstituteGilead SciencesFrancis Crick InstituteStanford UniversityInternational Association for the Study of Lung CancerMerckGlaxoSmithKlineRoyal SocietyBristol-Myers SquibbEli Lilly and CompanyAstraZenecaPfizerBoehringer IngelheimAmgenRoche
KeywordsLiquid biopsyMedicineLung cancerCancerOncologyAdenocarcinomaPersonalized medicineBiopsyPrecision medicineErlotinibInternal medicineBioinformaticsPathology

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)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.002

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 head0.041
GPT teacher head0.467
Teacher spread0.427 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations645
Published2021
Admission routes1
Has abstractno

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