Objective: This study aimed to enhance the quintessential "five temporomandibular disorder (TMD) symptoms" (5Ts) screener by incorporating frequency options and distinguishing between TMJ and muscle pain. The diagnostic accuracy along with cut-off points for the effective identification of TMDs was also established.
Methods: Participants, aged ≥18 years, were recruited from a university-based hospital. After completing surveys encompassing demographic data and the enhanced 5Ts (with frequency options [5Ts-F] and differentiation of TMJ/muscle pain [6Ts-F]), protocolized interviews and clinical examinations were performed following DC/TMD. The diagnostic accuracy and best cut-off points were determined with the area under the receiver operating characteristic curves (AUCs).
Results: 324 participants were recruited (mean age 30.0 ± 11.4 years). Among these, 86.4% had TMDs. 5Ts exhibited high diagnostic accuracy for detecting all TMDs (AUC = 0.92) with sensitivity/specificity values of 83.9%/88.6%. Both 5Ts-F and 6Ts-F had slightly better accuracy (AUCs = 0.95/0.96), comparable sensitivity, and superior specificity (97.7%) compared to 5Ts. The best cut-off points were 1.5 for 5Ts and 2.5 for 5Ts-F/6Ts-F.
Conclusions: Although all three TMD screeners presented high diagnostic accuracy, 5Ts-F/6Ts-F had notably improved specificity. 5Ts scores of >1.5 and 5Ts-F/6Ts-F scores of >2.5 are to be applied for screening the presence of TMDs.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/odi.14877 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Prospective validation study of a combined urine and plasma test for predicting high-grade prostate cancer in biopsy naïve men.
Scand J Urol
January 2025
Department of Urology, Odense University Hospital, Odense, Denmark; Academy of Geriatric Cancer Research (AgeCare), Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
Objective: Early and accurate diagnosis of prostate cancer (PC) is crucial for effective treatment. Diagnosing clinically insignificant cancers can lead to overdiagnosis and overtreatment, highlighting the importance of accurately selecting patients for further evaluation based on improved risk prediction tools. Novel biomarkers offer promise for enhancing this diagnostic process.
View Article and Find Full Text PDFIdentification of IGFBP3 and LGALS1 as potential secreted biomarkers for clear cell renal cell carcinoma based on bioinformatics analysis and machine learning.
Adv Clin Exp Med
January 2025
Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, USA.
Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC). Due to the lack of symptoms until advanced stages, early diagnosis of ccRCC is challenging. Therefore, the identification of novel secreted biomarkers for the early detection of ccRCC is urgently needed.
View Article and Find Full Text PDFA semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI.
Math Biosci Eng
December 2024
Department of Electronics and Communication Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates.
View Article and Find Full Text PDFResearch on bearing fault diagnosis based on a multimodal method.
Math Biosci Eng
December 2024
School of Information Engineering, Nantong Institute of Technology, Nantong 226002, Jiangsu, China.
As an essential component of mechanical systems, bearing fault diagnosis is crucial to ensure the safe operation of the equipment. However, vibration data from bearings often exhibit non-stationary and nonlinear features, which complicates fault diagnosis. To address this challenge, this paper introduces a novel multi-scale time-frequency and statistical features fusion model (MTSF-FM).
View Article and Find Full Text PDFEnhanced Pneumonia Detection in Chest X-Rays Using Hybrid Convolutional and Vision Transformer Networks.
Curr Med Imaging
January 2025
School of Life Sciences, Tiangong University, Tianjin 300387, China.
Objective: The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional diagnostics and treatment is limited.
Methods: The study developed a hybrid model architecture integrating CNNs with modified Swin Transformer blocks to work seamlessly within the same model.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!