IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0316827.html
   My bibliography  Save this article

Physical measures of physical functioning as prognostic factors in predicting outcomes for neck and thoracic pain: Protocol for a systematic review

Author

Listed:
  • Rabea Begum
  • Alison Rushton
  • Alaa El Chamaa
  • David Walton
  • Paul Parikh

Abstract

Background: Spinal pain is prevalent and burdensome worldwide. A large proportion of patients with neck and thoracic pain experience chronic symptoms, which can significantly impact their physical functioning. Therefore, it is important to understand factors predicting outcome to inform effective examination and treatment. Knowledge of physical measures of physical functioning as prognostic factors can enhance patient-centered care and aid decision-making. The evidence regarding physical outcome measures as prognostic factors for neck and thoracic pain is unclear. The objective of this study is to summarize the evidence for physical outcome measures of physical functioning as prognostic factors in predicting outcomes in people with neck and thoracic pain. Methods and analysis: This systematic review follows Cochrane guidelines and aligns with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Included studies will be prospective longitudinal cohort studies in which physical measures of physical functioning are explored as prognostic factors for adults with neck and thoracic pain. A comprehensive search will be performed in key databases (MEDLINE, EMBASE, CINAHL, Scopus, and Web of Science) and the grey literature, with hand searches of key journals, and the reference lists of included studies. Two reviewers will independently perform study selection, data extraction, risk of bias assessment (QUIPS, Quality in Prognostic Studies tool), and quality assessment (Grading of Recommendations Assessment, Development, and Evaluation). Implications: This systematic review will identify physical measures of physical functioning prognostic factors for neck and thoracic pain populations. Findings will inform researchers about gaps in existing evidence, and clinicians about factors to aid their clinical decisions and to enhance the overall quality of care for individuals with neck and thoracic pain.

Suggested Citation

  • Rabea Begum & Alison Rushton & Alaa El Chamaa & David Walton & Paul Parikh, 2025. "Physical measures of physical functioning as prognostic factors in predicting outcomes for neck and thoracic pain: Protocol for a systematic review," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-16, January.
  • Handle: RePEc:plo:pone00:0316827
    DOI: 10.1371/journal.pone.0316827
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0316827
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0316827&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0316827?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Rameeza Rashed & Katie Kowalski & David Walton & Afieh Niazigharemakhe & Alison Rushton, 2023. "Physical measures of physical functioning as prognostic factors to predict outcomes in low back pain: Protocol for a systematic review," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-11, December.
    2. Karel G M Moons & Joris A H de Groot & Walter Bouwmeester & Yvonne Vergouwe & Susan Mallett & Douglas G Altman & Johannes B Reitsma & Gary S Collins, 2014. "Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist," PLOS Medicine, Public Library of Science, vol. 11(10), pages 1-12, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Santiago Ferrière-Steinert & Joaquín Valenzuela Jiménez & Sebastián Heskia Araya & Thomas Kouyoumdjian & José Ramos-Rojas & Abraham I J Gajardo, 2024. "Early high-sensitivity troponin elevation in predicting short-term mortality in sepsis: A protocol for a systematic review with meta-analysis," PLOS ONE, Public Library of Science, vol. 19(10), pages 1-10, October.
    2. Jiaxin Li & Zijun Zhou & Jianyu Dong & Ying Fu & Yuan Li & Ze Luan & Xin Peng, 2021. "Predicting breast cancer 5-year survival using machine learning: A systematic review," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-23, April.
    3. Fangyue Chen & Piyawat Kantagowit & Tanawin Nopsopon & Arisa Chuklin & Krit Pongpirul, 2023. "Prediction and diagnosis of chronic kidney disease development and progression using machine-learning: Protocol for a systematic review and meta-analysis of reporting standards and model performance," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-10, February.
    4. Wanting Zu & Xuemiao Huang & Tianxin Xu & Lin Du & Yiming Wang & Lisheng Wang & Wenbo Nie, 2023. "Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review," PLOS ONE, Public Library of Science, vol. 18(6), pages 1-14, June.
    5. Lukas Higi & Angela Lisibach & Patrick E Beeler & Monika Lutters & Anne-Laure Blanc & Andrea M Burden & Dominik Stämpfli, 2021. "External validation of the PAR-Risk Score to assess potentially avoidable hospital readmission risk in internal medicine patients," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-14, November.
    6. Anna M van Boekel & Siri L van der Meijden & Sesmu M Arbous & Rob G H H Nelissen & Karin E Veldkamp & Emma B Nieswaag & Kim F T Jochems & Jeroen Holtz & Annekee van IJlzinga Veenstra & Jeroen Reijman , 2024. "Systematic evaluation of machine learning models for postoperative surgical site infection prediction," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-17, December.
    7. Fazel, Seena & Burghart, Matthias & Fanshawe, Thomas & Gil, Sharon Danielle & Monahan, John & Yu, Rongqin, 2022. "The predictive performance of criminal risk assessment tools used at sentencing: Systematic review of validation studies," Journal of Criminal Justice, Elsevier, vol. 81(C).
    8. Fisaha Haile Tesfay & Kathryn Backholer & Christina Zorbas & Steven J. Bowe & Laura Alston & Catherine M. Bennett, 2022. "The Magnitude of NCD Risk Factors in Ethiopia: Meta-Analysis and Systematic Review of Evidence," IJERPH, MDPI, vol. 19(9), pages 1-19, April.
    9. Shamil D. Cooray & Lihini A. Wijeyaratne & Georgia Soldatos & John Allotey & Jacqueline A. Boyle & Helena J. Teede, 2020. "The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal," IJERPH, MDPI, vol. 17(9), pages 1-20, April.
    10. Helder Novais Bastos & Nuno S Osório & António Gil Castro & Angélica Ramos & Teresa Carvalho & Leonor Meira & David Araújo & Leonor Almeida & Rita Boaventura & Patrícia Fragata & Catarina Chaves & Pat, 2016. "A Prediction Rule to Stratify Mortality Risk of Patients with Pulmonary Tuberculosis," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-14, September.
    11. Antonio Palazón-Bru & María José Prieto-Castelló & David Manuel Folgado-de la Rosa & Ana Macanás-Martínez & Emma Mares-García & María de los Ángeles Carbonell-Torregrosa & Vicente Francisco Gil-Guillé, 2020. "Development, and Internal, and External Validation of a Scoring System to Predict 30-Day Mortality after Having a Traffic Accident Traveling by Private Car or Van: An Analysis of 164,790 Subjects and ," IJERPH, MDPI, vol. 17(24), pages 1-13, December.
    12. Paulien Van Acker & Wim Van Biesen & Evi V Nagler & Muguet Koobasi & Nic Veys & Jill Vanmassenhove, 2021. "Risk prediction models for acute kidney injury in adults: An overview of systematic reviews," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-14, April.
    13. Sara J Baart & Veerle Dam & Luuk J J Scheres & Johanna A A G Damen & René Spijker & Ewoud Schuit & Thomas P A Debray & Bart C J M Fauser & Eric Boersma & Karel G M Moons & Yvonne T van der Schouw & on, 2019. "Cardiovascular risk prediction models for women in the general population: A systematic review," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-14, January.
    14. Hebatullah Abdulazeem & Sera Whitelaw & Gunther Schauberger & Stefanie J Klug, 2023. "A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data," PLOS ONE, Public Library of Science, vol. 18(9), pages 1-25, September.
    15. Hans Van Remoortel & Hans Scheers & Emmy De Buck & Winne Haenen & Philippe Vandekerckhove, 2020. "Prediction modelling studies for medical usage rates in mass gatherings: A systematic review," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-20, June.
    16. Mohsen Askar & Masoud Tafavvoghi & Lars Småbrekke & Lars Ailo Bongo & Kristian Svendsen, 2024. "Using machine learning methods to predict all-cause somatic hospitalizations in adults: A systematic review," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-21, August.
    17. repec:plo:pone00:0226480 is not listed on IDEAS
    18. Vieira, Bruno Hebling & Pamplona, Gustavo Santo Pedro & Fachinello, Karim & Silva, Alice Kamensek & Foss, Maria Paula & Salmon, Carlos Ernesto Garrido, 2022. "On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting," Intelligence, Elsevier, vol. 93(C).
    19. Magdalena Lagerlund & Juan Merlo & Raquel Pérez Vicente & Sophia Zackrisson, 2015. "Does the Neighborhood Area of Residence Influence Non-Attendance in an Urban Mammography Screening Program? A Multilevel Study in a Swedish City," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-21, October.
    20. Wei Zhang & Yun Tang & Huan Liu & Li ping Yuan & Chu chu Wang & Shu fan Chen & Jin Huang & Xin yuan Xiao, 2021. "Risk prediction models for intensive care unit-acquired weakness in intensive care unit patients: A systematic review," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-14, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0316827. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.