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Record W1994403941 · doi:10.3389/fonc.2014.00244

Review of EGFR TKIs in Metastatic NSCLC, Including Ongoing Trials

2014· review· en· W1994403941 on OpenAlexaff
Barbara Melosky

Bibliographic record

VenueFrontiers in Oncology · 2014
Typereview
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMedicineClinical trialEpidermal growth factor receptorOncologyLung cancerTyrosine kinaseInternal medicineFirst lineMutationCancer researchCancerReceptorBiology

Abstract

fetched live from OpenAlex

Recent clinical trials have demonstrated the efficacy of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) in the treatment of patients with advanced metastatic non-small cell lung cancer. Most of these recent trials were conducted in patients with EGFR mutation-positive tumors. As our knowledge of the EGFR mutation and its resistant pathways develops, the complexity of the situation expands. This article briefly reviews the pivotal trials leading to approval of EGFR TKIs in the first-line setting for patients with EGFR mutation-positive non-small cell lung carcinomas. It discusses the historical use of EGFR TKIs after the first-line setting in unselected patients and briefly describes ongoing trials.

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)0.001
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.144
GPT teacher head0.522
Teacher spread0.379 · 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 designSystematic review
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

Citations59
Published2014
Admission routes1
Has abstractyes

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