Post‐traumatic stress, prevalence of temporomandibular disorders in war veterans: systematic review with meta‐analysis
Abstract: Introduction: The physical and psychological effects of war are not always easy to detect, but they can be far‐reaching and long‐lasting. One of the physical effects that may result from war stress is temporomandibular disorder (TMD). Objective: To evaluate the prevalence of TMD sign and symptoms among war veterans diagnosed with PTSD. Methods: We systematically searched in Web of Science, PubMed and Lilacs for articles published from the inception until 30 December 2022. All documents were assessed for eligibility based on the following Population, Exposure, Comparator and Outcomes (PECO) model: (P) Participants consisted of human subjects. (E) The Exposure consisted of exposition to war. (C) The Comparison was between war veterans (subjects exposed to war) and subjects not exposed to war. (O) The Outcome consisted of presence of temporomandibular disorders sign or symptoms (we considered pain to muscle palpation in war veterans). Results: Forty studies were identified at the end of the research. We chose only four study to draw up the present systematic study. The included subjects were 596. Among them, 274 were exposed to war, whereas the remaining 322 were not exposed to war stress. Among those exposed to war, 154 presented sign/symptoms of TMD (56.2%) whereas only 65 of those not exposed to war (20.18%). The overall effect revealed that subjects exposed to war and diagnosed with PTSD had a higher prevalence of TMD signs (pain at muscle palpation) than controls (RR 2.21; 95% CI: 1.13–4.34), showing an association PTSD war‐related and TMD. Conclusions: War can cause lasting physical and psychological damage that can lead to chronic diseases. Our Results clearly demonstrated that war exposure, directly or indirectly, increases the risk of developing TMJ dysfunction and TMD sign/symptoms. [ABSTRACT FROM AUTHOR]
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.