Clinical trial reveals a new diagnostic algorithm for bruxism, indicating personalized management strategies.
activities. Materials and methods. The single-center, open-label, comparative clinical trial conducted at the Central Institute of Botulinum Therapy and Contemporary Neurology involved 120 people (10% males, 90% females; average age 29±12 years). Among them 100 had the diagnosis of bruxism (“bruxism” group) established in accordance with the criteria developed by the American Academy of Sleep Medicine (AASM), and 20 were conditionally healthy and had no history of masticatory muscle hyperfunction (control group). All the subjects underwent assessment in accordance with the reported clinical diagnostic algorithm, then the data were analyzed, and statistical processing of the results was performed. Results. The clinical diagnostic algorithm was developed, including the questionnaire survey, examination, measurement with a tape measure, imaging, palpation, ultrasonography, surface electromyography of masticatory muscles. A total of 120 subjects were assessed. By summarizing the results obtained we demonstrated reliability and feasibility of the algorithm. The study revealed the main diagnostic markers, triggers, and comorbidities. Conclusion. The proposed algorithm for assessment of patients with bruxism allows one to determine the likelihood of developing bruxism, not to miss the main diagnostic markers, to identify the factors triggering bruxism, and to ensure a more personalized approach to management of each particular patient. In medical practice, the algorithm can be applied partially, but remain clinically significant, including for the treatment dynamics monitoring. The use of algorithm for extensive research can help gain knowledge about bruxism for further study and phenotyping of the disorder.
No takes yet. Share an insight, caveat, or question.
Mizurova et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: