This study aims to translate a previously published English language questionnaire that assessed pain and discomfort after the extraction of primary teeth in children into Arabic, and evaluate its validity and reliability. All participating children ( = 120), aged 9 to 12-years-old, completed the 33-item Arabic version questionnaire after the extraction procedure had taken place. The questionnaire included three parts that were completed at three different times, namely, immediately, the first evening, and one week after the extraction procedure. Internal consistency, content validity, criterion validity, and factor analysis were performed. The results showed a good internal consistency (Cronbach's alpha = 0.83), acceptable criterion validity with a significantly strong correlation with the Visual Analog Scale (VAS), and satisfactory content validity (average content validity index (CVI = 0.90). The final factor model was comprised of four factors with an eigenvalue greater than 1, explaining 70% of the common variance. The identified factors were labeled as follows: Factor 1-analgesic consumption; Factor 2-expression of discomfort from the extraction site; Factor 3-perception of masticatory capability; and Factor 4-pain/discomfort from the dental extraction procedure. Based on the results, a shorter form of the questionnaire had satisfactory psychometric characteristics and can be used with children within the selected age group.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711770 | PMC |
http://dx.doi.org/10.3390/dj8040120 | DOI Listing |
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Pharmazie
December 2024
Department of Pharmacology and Toxicology, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia.
: Major Depressive Disorder (MDD) is a prevalent and debilitating mental disorder that has been linked to hyperhomocysteinemia and folate deficiency. These conditions are influenced by the methylenetetrahydrofolate reductase () gene, which plays a crucial role in converting homocysteine to methionine and is essential for folate metabolism and neurotransmitter synthesis, including serotonin. : This study explored the association between and polymorphisms among Saudi MDD patients attending the Erada Complex for Mental Health and Erada Services outpatient clinic in Jeddah, Saudi Arabia.
View Article and Find Full Text PDFAnn Surg Oncol
January 2025
Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: Anaplastic thyroid cancer (ATC) is a highly lethal disease, often diagnosed with advanced locoregional and distant metastases, resulting in a median survival of just 3-5 months. This study determines the stratified effectiveness of baseline treatments in all combinations, enabling precise prognoses prediction and establishing benchmarks for advanced therapeutic options.
Methods: The study extracted a cohort of pathologically confirmed ATC patients from the Surveillance, Epidemiology, and End Results program.
J Imaging Inform Med
January 2025
Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai, 200080, China.
The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3400 Spruce St., Philadelphia, PA, 19104, USA.
Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!