Exploring clinical pathways to a metastatic lung cancer diagnosis: A latent class analysis using North Carolina multi-payer claims data

Interventions tailored to utilization patterns-particularly for low-contact and emergency-based care users-may improve diagnostic equity, biomarker testing, and outcomes.

Authors

Institutions

Publication Details

Journal
Cancer Epidemiology Biomarkers & Prevention
Published
2026-03-26
DOI
https://doi.org/10.1158/1055-9965.epi-25-1859
Primary Topic
Lung Cancer Treatments and Mutations
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring clinical pathways to a metastatic lung cancer diagnosis: A latent class analysis using North Carolina multi-payer claims data

Lorinda A. Coombs, Caroline A. Thompson, K. Reeder-Hayes, J. Weiss et al.
Cancer Epidemiology Biomarkers & Prevention
Lung Cancer Treatments and Mutations
article

Exploring clinical pathways to a metastatic lung cancer diagnosis: A latent class analysis using North Carolina multi-payer claims data

Lorinda A. Coombs, Caroline A. Thompson, K. Reeder-Hayes, J. Weiss, Xi Zhou, Fernando Diaz, Jennifer L. Lund
article en

Abstract

Interventions tailored to utilization patterns-particularly for low-contact and emergency-based care users-may improve diagnostic equity, biomarker testing, and outcomes.

Cancer Epidemiology Biomarkers & Prevention
University of North Carolina at Chapel Hill (US), University of North Carolina Health Care (US), San Diego State University (US), The US Oncology Network (US)
Openalex Percentile: Top 7%
Lung Cancer Treatments and Mutations
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.