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Record W2803149877 · doi:10.6004/jnccn.2018.0046

Targeted Therapy for Patients With Metastatic Non–Small Cell Lung Cancer

2018· article· en· W2803149877 on OpenAlexaff
Karen L. Reckamp

Bibliographic record

VenueJournal of the National Comprehensive Cancer Network · 2018
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsSeagen (Canada)
FundersAriad Pharmaceuticals
KeywordsMedicineAlectinibOsimertinibROS1Targeted therapyLung cancerOncologyAnaplastic lymphoma kinaseErlotinibCeritinibInternal medicineTyrosine-kinase inhibitorTyrosine kinaseCancer researchCancerEpidermal growth factor receptorAdenocarcinomaReceptor

Abstract

fetched live from OpenAlex

Molecular testing is recommended for initial diagnosis in patients with non–small cell lung cancer (NSCLC), according to the updated NCCN Guidelines, because targeted therapies are available that can improve patient outcomes. Targeted therapies are currently approved for EGFR mutations, ALK and ROS1 gene rearrangements, and BRAF mutations, with the list of emerging “actionable” targets growing. The 2018 NCCN Guidelines for NSCLC incorporate new therapies, including the EGFR tyrosine kinase inhibitor osimertinib and the ALK inhibitor alectinib, as first-line preferences.

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 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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.003

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 head0.025
GPT teacher head0.339
Teacher spread0.314 · 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

Citations28
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the National Comprehensive Cancer NetworkSame topicLung Cancer Treatments and MutationsFrench-language works237,207