Evidence map›Paper›PMID 42170348›Full record

ArticleGynecologic oncology reports2026

Gynecologic cancers in veterans and military service members compared to non-veterans, 2004-2021: A National cancer database study.

Anna Jo Bodurtha Smith, D'Nyla Dean, Xingmei Wang, Leah L Zullig, Haley A Moss

Abstract read
In one paragraph

Article in Gynecologic oncology reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Anna Jo Bodurtha SmithDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Pennsylvania Health Systems, Philadelphia, PA, USA.
D'Nyla DeanDepartment of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA.
Xingmei WangDepartment of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Leah L ZulligDepartment of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA.
Haley A MossDivision of Gynecologic Oncology, Department of Obstetrics and Gynecology, Duke University School of Medicine, Durham, NC, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Women are the fastest growing demographic within the military-Veteran population in the United States. Our objective was to characterize gynecologic cancer occurrence, and trends over time, in Veterans and service members compared to non-veterans. Methods: We performed a retrospective cohort study using National Cancer database comparing cancer occurrence, age at diagnosis, histology, and survival among veterans and service members compared to non-veterans from 2004 to 2021. Results: Annual new gynecologic cancer diagnoses increased among Veterans from 301 in 2004 to 883 in 2021. Veterans now account for 1% of gynecologic cancer diagnoses nationwide. Veterans were diagnosed with gynecologic cancers at younger ages and earlier stages than non-veterans. Cancer histologies differed from non-veterans with endometrioid uterine cancer, germ cell ovarian cancer, and cervical adenocarcinoma being more common in Veterans. Overall survival was similar among Veterans and non-Veterans. Conclusions: As the proportion of women in the military has grown, gynecologic cancer diagnoses and survivorship among service members and Veterans are increasing. Further work is needed to understand the epidemiology of gynecologic cancer and optimize gynecologic cancer prevention and care delivery for service members and Veterans.

Identifiers

PMID42170348
PMCPMC13187539

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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.