ArticleHealthcare (Basel, Switzerland)2023
Musculoskeletal Injury Risk Stratification: A Traffic Light System for Military Service Members.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
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Who cites it
6 citing papers in PubMed.
- A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes.Sports medicine (Auckland, N.Z.) · 2025Article
- Model for Musculoskeletal Injury Risk Factors Among US Army Basic Combat Trainees.JAMA network open · 2025Article
- Association Between Pregnancy and Musculoskeletal Conditions in Active-Duty Military Service Members.Journal of athletic training · 2025Article
- A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes.medRxiv : the preprint server for health sciences · 2025Article
- Movement Clearing Screens for Military Service Member Musculoskeletal Injury Risk Identification.Journal of athletic training · 2025Article
- A machine learning framework to classify musculoskeletal injury risk groups in military service members.Frontiers in artificial intelligence · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Risk factor identification is a critical first step in informing musculoskeletal injury (MSKI) risk mitigation strategies. This investigation aimed to determine if a self-reported MSKI risk assessment can accurately identify military service members at greater MSKI risk and determine whether a traffic light model can differentiate service members' MSKI risks. A retrospective cohort study was conducted using existing self-reported MSKI risk assessment data and MSKI data from the Military Health System. A total of 2520 military service members (2219 males: age 23.49 ± 5.17 y, BMI 25.11 ± 2.94 kg/m
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Registered trials
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.