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Record W4284884980 · doi:10.1016/j.ejca.2022.05.023

Nazartinib for treatment-naive EGFR-mutant non−small cell lung cancer: Results of a phase 2, single-arm, open-label study

2022· article· en· W4284884980 on OpenAlexaff
Daniel S.W. Tan, Sang‐We Kim, Santiago Ponce Aix, Lecia V. Sequist, Egbert F. Smit, James Chih‐Hsin Yang, Toyoaki Hida, Ryo Toyozawa, Enriqueta Felip, Juergen Wolf, Christian Grohé, Natasha B. Leighl, Gregory J. Riely, Xiaoming Cui, Mike Zou, Samson Ghebremariam, Leslie O’Sullivan-Djentuh, Riccardo Belli, Monica Giovannini, Dong‐Wan Kim

Bibliographic record

VenueEuropean Journal of Cancer · 2022
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsPrincess Margaret Cancer Centre
FundersEli Lilly and CompanyOno PharmaceuticalIgnytaChugai PharmaceuticalEMD SeronoGenentechDaiichi Sankyo EuropeServierSanofiKyowa Hakko KirinEisaiPuma BiotechnologyBoehringer Ingelheim JapanTaiho PharmaceuticalMirati TherapeuticsNational Medical Research CouncilKisseiGlaxoSmithKlineNovartis Institutes for BioMedical ResearchAmgenDainippon Sumitomo PharmaPfizerNovartis Pharmaceuticals CorporationAstraZenecaBristol-Myers Squibb
KeywordsMedicineRashInternal medicineAdverse effectClinical endpointMaculopapular rashDiscontinuationBrain metastasisLung cancerResponse Evaluation Criteria in Solid TumorsOncologyGastroenterologyPhases of clinical researchT790Mnon-small cell lung cancer (NSCLC)CancerSurgeryToxicityEpidermal growth factor receptorMetastasisGefitinibClinical trial

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)0.002
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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0020.003
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 head0.064
GPT teacher head0.400
Teacher spread0.335 · 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 designNon-randomized trial
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

Citations18
Published2022
Admission routes1
Has abstractno

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Same venueEuropean Journal of CancerSame topicLung Cancer Treatments and MutationsFrench-language works237,207