Evidence map›Paper›PMID 41938601›Full record

ArticleNature. Mental health2026

Predicting Firearm Suicide among US Army Veterans Transitioning from Active Service.

Claire Houtsma, Chris J Kennedy, Howard Liu, Emily R Edwards, Nancy A Sampson, Joe C Geraci, Brian P Marx, Matthew K Nock, James Wagner, Murray B Stein and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature. Mental health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

12 authors.

Claire HoutsmaSoutheast Louisiana Veterans Health Care System, New Orleans, LA, USA.
Chris J KennedyDepartment of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
Howard LiuDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Emily R EdwardsVISN 2 Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, USA.
Nancy A SampsonDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA.
Joe C GeraciVISN 2 Mental Illness Research, Education and Clinical Center, U.S. Department of Veterans Affairs, Washington, District of Columbia, USA.
Brian P MarxBehavioral Science Division, National Center for PTSD, VA Boston Healthcare System. Boston, MA, USA.
Matthew K NockDepartment of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
James WagnerSurvey Research Center, Institute for Social Research, University of Michigan-Ann Arbor, Ann Arbor, MI, USA.
Murray B SteinDepartments of Psychiatry and School of Public Health, University of California San Diego, La Jolla, CA, USA.
Robert J UrsanoCenter for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University School of Medicine, Bethesda, MD, USA.
Ronald C KesslerDepartment of Health Care Policy, Harvard Medical School, Boston, MA, USA.

Funding

Modifiable Risk and Protective Factors for Suicidal Behaviors in the US ArmyU01MH087981 · NIMH · HENRY M. JACKSON FDN FOR THE ADV MIL/MED · PI STEIN, MURRAY B., URSANO, ROBERT J. · 2009 to 2013
$65.0M
Improving emergency psychiatric care through machine learning, causal inference, and psychometricsK01MH135131 · NIMH · MASSACHUSETTS GENERAL HOSPITAL · PI Christopher James Kennedy · 2024 to 2026
$544k
NIMH NIH HHS K01 MH135131NIMH NIH HHS U01 MH087981
6 · The paper itself

Abstract

United States (US) Veterans are significantly more likely to die by suicide than civilians. Machine learning (ML) models have been developed to target high-risk transitioning service members for suicide prevention interventions to reduce Veteran suicides. These models are suicide method-agnostic. However, firearms are involved in most Veteran suicides, and firearm-specific preventions exist. We used data from US Army Veterans from 2010-2019 (

Identifiers

PMID41938601
PMCPMC13047788

What OpenQuestion holds

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Registered trials

None linked

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.