Purpose: To explore the soft tissue, marginal bone, and prosthetic complications (if any) of Astra Tech, Brånemark, and ITI implants supporting fixed prostheses during an observation period of 2 years.
Materials: The study comprised 26 patients, who received 42 Astra Tech, 36 Brånemark, and 29 ITI implants. After 3 months of healing, abutment connections were performed for Astra Tech and Brånemark implants, and fixed prostheses were delivered to the patients at 4 months. At 6-month, 1-year, and 2-year recall appointments, plaque index, periimplant inflammation index, and bleeding index scores, were recorded. The marginal bone levels were also measured at 2-year recall by means of radiographic evaluation, and prosthetic complications were recorded throughout the study.
Results: All implants survived during the 2-year observation period. The plaque index and periimplant inflammation index scores around Brånemark implants were higher than ITI and Astra Tech implants in the first year of function (P > 0.05). Marginal bone loss around ITI and Astra Tech implants was similar at 2 years (P > 0.05). The marginal bone loss around Brånemark implants was higher than Astra Tech implants (P < 0.05) but similar to ITI implants at 2-year recall appointment (P > 0.05). Fixed prostheses supported by ITI and Astra Tech implants did not experience prosthetic complications, and only 1 patient of the Brånemark group had porcelain veneer fracture.
Conclusions: Astra Tech, Brånemark, and ITI implants supporting fixed prostheses had same survival rates (100%) in this study. ITI and Astra Tech implants had similar changes in marginal bone levels, whereas Brånemark implants had higher marginal bone loss, particularly in the first year of function.
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http://dx.doi.org/10.1097/ID.0b013e3181f57110 | DOI Listing |
JTCVS Tech
December 2024
Division of Thoracic Surgery, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada.
Objective: Endobronchial ultrasound-guided transbronchial needle aspiration is a vital tool for mediastinal and hilar lymph node staging in patients with lung cancer. Despite its high diagnostic performance and safety, it has a limited negative predictive value. Our objective was to evaluate the diagnostic performance of deep learning-based prediction of lung cancer lymph node metastases using convolutional neural networks developed from automatically extracted images of endobronchial ultrasound videos without supervision of the lymph node location.
View Article and Find Full Text PDFJ Prosthodont
December 2024
Department of Prosthodontics, Hacettepe University Faculty of Dentistry Sıhhiye, Ankara, Turkey.
Nat Commun
December 2024
Department of Internal Medicine, University of Central Florida, College of Medicine, Orlando, FL, USA.
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