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Record W4206585470 · doi:10.1158/1055-9965.epi-21-0747

Accounting for <i>EGFR</i> Mutations in Epidemiologic Analyses of Non–Small Cell Lung Cancers: Examples Based on the International Lung Cancer Consortium Data

2022· article· en· W4206585470 on OpenAlexafffund
Sabine Schmid, Mei Jiang, M. Catherine Brown, Aline Fusco Fares, Miguel García-Pardo, Joelle Soriano, Mei Dong, Sera Thomas, Takashi Kohno, Letícia Ferro Leal, Nancy Diao, Juntao Xie, Zhichao Wang, Давид Заридзе, Ivana Holcátová, Jolanta Lissowska, Beata Świątkowska, Dana Mateș, Milan Savić, Angela S. Wenzlaff, Curtis C. Harris, Neil E. Caporaso, Hongxia Ma, Guillermo Fernández‐Tardón, Matthew J Barnett, Gary E. Goodman, Michael P.A. Davies, Mónica Pérez‐Ríos, Fiona Taylor, Eric J. Duell, Ben Schoettker, Hermann Brenner, Angeline S. Andrew, Angela Cox, Alberto Ruano‐Raviña, John K. Field, Loı̈c Le Marchand, Ying Wang, Chu Chen, Adonina Tardón, Sanjay Shete, Matthew B. Schabath, Hongbing Shen, Maria Teresa Landi, Bríd M. Ryan, Ann G. Schwartz, Lihong Qi, Lori C. Sakoda, Paul Brennan, Ping Yang, Jie Zhang, David C. Christiani, Rui Manuel Reis, Kouya Shiraishi, Rayjean J. Hung, Wei Xu, Geoffrey Liu

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2022
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsLunenfeld-Tanenbaum Research InstitutePublic Health OntarioUniversity of TorontoUniversity of OttawaPrincess Margaret Cancer Centre
FundersNational Cancer InstituteNational Institutes of HealthWorld Health OrganizationSwiss Cancer Research FoundationFinanciadora de Estudos e ProjetosPrincess Margaret Cancer FoundationHospital de Câncer de Barretos
KeywordsOncologyMedicineLung cancerConcordanceInternal medicineImputation (statistics)Missing dataStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: Somatic EGFR mutations define a subset of non-small cell lung cancers (NSCLC) that have clinical impact on NSCLC risk and outcome. However, EGFR-mutation-status is often missing in epidemiologic datasets. We developed and tested pragmatic approaches to account for EGFR-mutation-status based on variables commonly included in epidemiologic datasets and evaluated the clinical utility of these approaches. METHODS: Through analysis of the International Lung Cancer Consortium (ILCCO) epidemiologic datasets, we developed a regression model for EGFR-status; we then applied a clinical-restriction approach using the optimal cut-point, and a second epidemiologic, multiple imputation approach to ILCCO survival analyses that did and did not account for EGFR-status. RESULTS: Of 35,356 ILCCO patients with NSCLC, EGFR-mutation-status was available in 4,231 patients. A model regressing known EGFR-mutation-status on clinical and demographic variables achieved a concordance index of 0.75 (95% CI, 0.74-0.77) in the training and 0.77 (95% CI, 0.74-0.79) in the testing dataset. At an optimal cut-point of probability-score = 0.335, sensitivity = 69% and specificity = 72.5% for determining EGFR-wildtype status. In both restriction-based and imputation-based regression analyses of the individual roles of BMI on overall survival of patients with NSCLC, similar results were observed between overall and EGFR-mutation-negative cohort analyses of patients of all ancestries. However, our approach identified some differences: EGFR-mutated Asian patients did not incur a survival benefit from being obese, as observed in EGFR-wildtype Asian patients. CONCLUSIONS: We introduce a pragmatic method to evaluate the potential impact of EGFR-status on epidemiological analyses of NSCLC. IMPACT: The proposed method is generalizable in the common occurrence in which EGFR-status data are missing.

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)0.092
metaresearch head score (Gemma)0.151
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.484

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0920.151
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.156
GPT teacher head0.460
Teacher spread0.304 · 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 designObservational
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

Citations2
Published2022
Admission routes2
Has abstractyes

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