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Record W2912998652 · doi:10.1038/s41586-019-0974-0

Author Correction: HER kinase inhibition in patients with HER2- and HER3-mutant cancers

2019· erratum· en· W2912998652 on OpenAlexfundno aff
David M. Hyman, Sarina A. Piha‐Paul, Helen Won, Jordi Rodón, Cristina Saura, Geoffrey I. Shapiro, Dejan Juric, David I. Quinn, Víctor Moreno, Bernard Doger, Ingrid A. Mayer, Valentina Boni, Emiliano Calvo, Sherene Loi, A. Craig Lockhart, Joseph P. Erinjeri, Maurizio Scaltriti, Gary A. Ulaner, Juber Patel, Jiabin Tang, Hannah Beer, S. Duygu Selçuklu, Aphrothiti J. Hanrahan, Nancy Bouvier, Myra Melcer, Rajmohan Murali, Alison M. Schram, Lillian M. Smyth, Komal Jhaveri, Bob T. Li, Alexander Drilon, James J. Harding, Gopa Iyer, Barry S. Taylor, Michael F. Berger, Richard E. Cutler, Feng Xu, Anna Butturini, Lisa D. Eli, Grace Mann, Cynthia Farrell, Alshad S. Lalani, Richard Bryce, Carlos L. Arteaga, Funda Meric‐Bernstam, José Baselga, David B. Solit

Bibliographic record

VenueNature · 2019
Typeerratum
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsnot available
FundersLoxo OncologyGenentechEMD SeronoFundació Institut de Recerca Hospital Universitari Vall d’HebronEli Lilly and CompanyChinese University of Hong KongJuno TherapeuticsEisaiDaiichi-SankyoPuma BiotechnologyAminex TherapeuticsSymphogenPfizerIncyteIpsenFive Prime TherapeuticsLes Laboratories Pierre FabreBioMarin PharmaceuticalSanofiSierra OncologyGenomic HealthClovis OncologyVarian Medical SystemsMemorial Sloan-Kettering Cancer CenterGlaxoSmithKlineSpectrum PharmaceuticalsJounce TherapeuticsCelgeneServierFoghorn TherapeuticsAstraZenecaBristol-Myers Squibb
KeywordsMutantCancer researchKinaseInternal medicineBiologyOncologyMedicineGeneticsGene

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)0.003
metaresearch head score (Gemma)0.039
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.039
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0030.001
Research integrity0.0080.012
Insufficient payload (model declined to judge)0.0200.011

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 head0.010
GPT teacher head0.311
Teacher spread0.300 · 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
GenreOther

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

Citations15
Published2019
Admission routes1
Has abstractno

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