Evidence map›Paper›PMID 41804363›Full record

ArticleBreast cancer (Dove Medical Press)2026

Drivers of Variation in the Rate of Radiotherapy Following Lumpectomy in the Military Health System.

Mark Louie F Ramos, Nicholas G Zaorsky, William Patrick Luan, William A Calo, Alison Chetlen, Eugene J Lengerich, Guangqing Chi, Joel E Segel

Abstract read
In one paragraph

Article in Breast cancer (Dove Medical Press), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Mark Louie F RamosDepartment of Health Policy and Administration, Penn State University, State College, PA, USA.ORCID 0000-0002-5069-2397
Nicholas G ZaorskyDepartment of Radiation Oncology, University Hospitals Seidman Cancer Center, Case Western Reserve School of Medicine, Cleveland, OH, USA.
William Patrick LuanInstitute for Defense Analyses, Alexandria, VA, USA.
William A CaloPenn State Cancer Institute, Hershey, PA, USA.
Alison ChetlenPenn State Cancer Institute, Hershey, PA, USA.
Eugene J LengerichPenn State Cancer Institute, Hershey, PA, USA.
Guangqing ChiDepartment of Geography, Indiana University, Bloomington, IN, USA.ORCID 0000-0003-0888-7964
Joel E SegelDepartment of Health Policy and Administration, Penn State University, State College, PA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Radiation therapy (RT) following lumpectomy has been shown to improve survival. However, little is known about drivers of RT within the Military Health System (MHS). We examined how factors previously shown to affect RT rates after lumpectomy in the civilian population and factors exclusive to service members such as rank, service branch, and MHS catchment area are associated with undergoing RT within 6 months after lumpectomy. Materials and methods: We conducted a retrospective cohort study using the 2007-2019 MHS Data Repository (MDR) to obtain data from 9681 service members or dependents who had lumpectomy to treat breast cancer and remained in the system at least 6 months after their lumpectomy. We used Cox-Proportional Hazards to model time until their first RT session and examined how demographic and service-related factors affected rates of RT following lumpectomy. Results: Within the sample, 57.4% of lumpectomy patients received RT within 6 months after surgery. Receiving timely RT was found to be positively associated with older age (Cox Model Benefit Ratio 95% CI [1.01, 1.02]), senior rank (Benefit Ratio 95% CI [1.00, 1.31]), and having a Mental Health diagnosis (Benefit Ratio 95% CI [1.40, 1.60]) when controlling for all other variables in the model. Using catchment area RT rates from 2007 to 2017 and modelling data from 2018 to 2019, it was found that timely RT is positively associated with being in the catchment areas at the top quartile of historical RT rates (Benefit Ratio 95% CI [1.06, 1.47]). Conclusion: We identify important variation in receiving RT after lumpectomy both when comparing MHS rates to known rates in the general population and when comparing rates across catchment areas where military personnel are stationed. Lumpectomy patients in the MHS who are stationed in catchment areas with higher historical RT rates are themselves more likely to undergo RT.

Indexed as

lumpectomymilitary health systemradiation therapyretrospective cohortstandard of care

Identifiers

PMID41804363
PMCPMC12967452

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.