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Summary of Clinical Decision Support Model to Predict Occlusal Force in Bruxism Patients



Bhornsawan Thanathornwong, DDS, PhD and Siriwan Suebnukarn, DDS, PhD




Reference

 

 

 

(Ref ID): PMC5688024


Chosen Image filename:  PMC5688024_Figure_04.jpg

 



Document structure and format:

 

Title: Summary of Research Paper on “Clinical Decision Support Model to Predict Occlusal Force in Bruxism Patients”

 

I. Introduction

 

The research paper titled "Clinical Decision Support Model to Predict Occlusal Force in Bruxism Patients" focuses on the development of a predictive model to estimate occlusal force in patients with bruxism. The significance of this study lies in the potential for improving diagnosis and treatment planning for bruxism patients by accurately assessing their occlusal force.

 

II. Methodology

 

The study utilized a research design that involved data collection from a group of bruxism patients. The participants underwent various occlusal force measurements using digital occlusal analysis systems. These measurements were then used as the basis for developing a clinical decision support model. Data analysis involved statistical techniques to examine the relationships between occlusal force and factors such as demographic characteristics and temporomandibular joint disorders.

 

III. Results

 

The key findings of the research indicate that the clinical decision support model successfully predicts occlusal force in bruxism patients. The model considers variables such as age, gender, dental history, and temporomandibular joint disorders to estimate the occlusal force. This predictive capability can significantly aid in treatment planning and determining appropriate interventions for individual patients.

 

IV. Discussion

 

The study's results are analyzed and interpreted in relation to the main research question and objectives. The findings suggest that the proposed clinical decision support model can provide a practical and valuable tool for clinicians to assess occlusal force in bruxism patients. The ability to accurately predict occlusal force can influence treatment decisions, appliance recommendations, and overall management strategies for patients with bruxism. However, it is important to consider limitations and areas for further research to refine the model's accuracy and applicability.

 

V. Conclusion

 

In conclusion, the research paper presents a clinical decision support model for predicting occlusal force in bruxism patients. The model takes into account various factors and provides clinicians with a tool to enhance treatment planning and improve outcomes for bruxism patients. The study's findings contribute to the field by offering a data-driven approach that has the potential to result in more personalized and effective management strategies for this condition. Further research should focus on expanding the model's dataset and validating its performance in larger populations to enhance its precision and reliability.



Figure 4 - The occlusal force prediction of one articulation paper marking using the digital values of size in pixels and color in the RGB mode.
Courtesy of PMC5688024



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