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Record W3214907506 · doi:10.1007/s40291-021-00563-1

Worldwide Prevalence of Epidermal Growth Factor Receptor Mutations in Non-Small Cell Lung Cancer: A Meta-Analysis

2021· review· en· W3214907506 on OpenAlexaff
Barbara Melosky, Kato Kambartel, Maik Häntschel, M Bennetts, Dana J. Nickens, Julia Brinkmann, Antonin Kayser, Michael Moran, Federico Cappuzzo

Bibliographic record

VenueMolecular Diagnosis & Therapy · 2021
Typereview
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsUniversity of British ColumbiaBC Cancer Agency
FundersPfizer
KeywordsLung cancerMeta-analysisEpidermal growth factor receptorHuman geneticsOncologyMedicineMolecular medicineInternal medicineCancerBiologyBioinformaticsGeneticsCell cycleGene

Abstract

fetched live from OpenAlex

BACKGROUND: Identification of variable epidermal growth factor receptor (EGFR) gene mutations in non-small cell lung cancer (NSCLC) is important for the selection of appropriate targeted therapies. This meta-analysis was conducted to provide a worldwide overview of EGFR mutation and submutation (specifically exon 19 deletions, exon 21 L858R substitutions, and others) prevalence, and identify important covariates that influence EGFR mutation status in patients with advanced NSCLC to address this clinical data gap. METHODS: in Ovid were searched for studies published between 2004 and 2019 with cohorts of ≥ 50 adults with EGFR mutations, focusing on stage III/IV NSCLC (≤ 20% of patients with stage I/II NSCLC). Linear mixed-effects models were fitted to EGFR mutation endpoints using logistic transformation (logit), assuming a binomial distribution. The model included terms for an intercept reflecting European studies and further additive terms for other continents. EGFR submutations examined were exon 19 deletions, exon 21 L858R substitutions, and others. RESULTS: Of 3969 abstracts screened, 57 studies were included in the overall EGFR mutation analysis and 74 were included in the submutation analysis relative to the overall EGFR mutation population (Europe, n = 12; Asia, n = 51; North America, n = 5; Central America, n = 1; South America, n = 1; Oceania, n = 1; Global, n = 3). The final overall EGFR mutations model estimated Asian and European prevalence of 49.1% and 12.8%, respectively, and included an additive covariate for the proportion of male patients in a study. There were no significant covariates in the submutation analyses. Most submutations were actionable: exon 19 deletions (49.2% [Asia]; 48.4% [Europe]); exon 21 L858R substitutions (41.1% [Asia]; 29.9% [Europe]). CONCLUSIONS: Although EGFR mutation prevalence was higher in Asian than Western countries, data support worldwide testing for EGFR overall and submutations to inform appropriate targeted treatment decisions.

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)0.014
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.042
Bibliometrics0.0060.008
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.052
GPT teacher head0.378
Teacher spread0.326 · 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 designMeta-analysis
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

Citations235
Published2021
Admission routes1
Has abstractyes

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