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Record W2808999647 · doi:10.1126/scitranslmed.aao2301

Afatinib restrains K-RAS–driven lung tumorigenesis

2018· article· en· W2808999647 on OpenAlexaff
Herwig P. Moll, Klemens Pranz, Mónica Musteanu, Beatrice Grabner, Natascha Hruschka, Julian Mohrherr, Petra Aigner, Patricia Stiedl, Luka Brčić, Viktória László, Daniel Schramek, Richard Moriggl, Robert Eferl, Judit Moldvay, Katalin Dezső, Pedro P. López‐Casas, Dagmar Stoiber, Manuel Hidalgo, Josef Penninger, Maria Sibilia, Balázs Győrffy, Mariano Barbacid, Balázs Döme, Helmut Popper, Emilio Casanova

Bibliographic record

VenueScience Translational Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
FundersAustrian Science Fund
KeywordsCarcinogenesisAfatinibCancer researchLungErbBLung cancerSignal transductionMedicineBiologyChemistryReceptorInternal medicineEpidermal growth factor receptorCell biologyCancerGefitinib

Abstract

fetched live from OpenAlex

K-RAS –mutated lung adenocarcinomas depend on ERBB signaling, and pan-ERBB inhibitors impair K-RAS–driven lung tumorigenesis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.398
Teacher spread0.369 · 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 teacher head, not a consensus.

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

Citations126
Published2018
Admission routes1
Has abstractyes

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