Objective: To evaluate and compare the performance of OpenAI's ChatGPT-4 and Microsoft Copilot in providing information on 3D-printed orthodontic appliances, with a focus on the accuracy, completeness of the content, and response generation time.
Methods: This cross-sectional study proceeded in five stages. Initially, three orthodontists created a total of 125 questions concerning 3D printed orthodontic appliances of which 105 questions were finalized to be incorporated into the study by a panel of senior orthodontists. These questions were subsequently organized into 15 distinct domains. Both chatbots were presented with the questions under consistent conditions, using the same laptop and internet setup. A stopwatch was used to record response times. The responses were anonymized and evaluated by seven orthodontists with extensive experience, who scored accuracy and completeness based on standardized tools. Through discussion, evaluators reached a consensus on each score, ensuring reliability.
Results: Spearman's correlation revealed a moderate to strong negative correlation between accuracy and completeness for both chatbots (p≤0.001). The negative correlation observed between accuracy and completeness scores, particularly prominent in Copilot, indicates a trade-off between these qualities in some responses. Mann-Whitney U tests confirmed significant differences in accuracy and completeness between the chatbots (p≤0.001), though response time differences were not statistically significant (p=0.204). Cohen's Kappa results implied little to no consistency between the two models on the assessed parameters (p>0.05).
Conclusion: ChatGPT-4 outperformed Microsoft Copilot in accuracy and completeness, providing more precise and comprehensive information on 3D-printed orthodontic appliances demonstrating a greater ability to handle complex, and detailed requests in this area.
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http://dx.doi.org/10.1016/j.ortho.2025.100992 | DOI Listing |
J AOAC Int
March 2025
Department of Chemistry, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, K1S 5B6 Canada.
Background: Plant-based milk alternatives (PBMA) are increasingly popular due to rising lactose intolerance and environmental concerns over traditional dairy products. However, limited efforts have been made to develop rapid authentication methods to verify their biological origin.
Objective: In this study, we developed a rapid, on-site analytical method for the authentication and identification of PBMA made by six different plant species utilizing a portable Raman spectrometer coupled with machine learning.
J Pediatr Orthop
March 2025
Shriners Children's Portland, Portland, OR.
Background: Toe walking is prevalent among children, affecting 5% to 24% of the pediatric population. Clinicians rely on parental reports of frequency of toe walking to guide clinical decision making and outcomes assessment. However, recall accuracy and differing environments challenge the reliability of parental reports.
View Article and Find Full Text PDFMem Cognit
March 2025
Section Forensic Psychology, Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience Maastricht University, Universiteitssingel 40, PO Box 616, 6200 MD, Maastricht, The Netherlands.
Recognizing masked faces is a challenge. Researchers have explored congruency-based approaches to improve face matching, with promising results. Here, we investigated whether congruency between the encoding and the retrieval conditions can improve masked face recognition when only the eyes are visible under conditions of high and low memory load.
View Article and Find Full Text PDFRadiol Med
March 2025
Department of Radiology, Suez Canal University, Ismailia, Egypt.
Background: The purpose of this study is to assess the usefulness of the novel abbreviated MR (AB-MR) protocol in the screening of women with an intermediate risk of breast cancer. Sixty women with a Tyrer-Cuzick model-determined intermediate risk of breast cancer underwent AB-MR, mammography, and tomosynthesis examinations; as an auxiliary procedure, ultrasound imaging was carried out. Every modality was allocated a final BI-RADS category.
View Article and Find Full Text PDFDrugs
March 2025
Springer Nature, Mairangi Bay, Private Bag 65901, Auckland, 0754, New Zealand.
Revumenib (Revuforj) is an oral, first-in-class menin inhibitor developed by Syndax Pharmaceuticals for the treatment of KMT2A-rearranged (KMT2Ar) acute leukaemia, NPM1-mutated (NPM1m) acute myeloid leukaemia (AML) and solid tumours. The interaction between menin and the KMT2A protein complex leads to aberrant gene expression, driving leukaemogenic transcription. By blocking this interaction, revumenib promotes differentiation and exerts antileukaemic activity in KMT2Ar acute leukaemias and other menin inhibition-sensitive leukaemias.
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