(1) Background: Given the existing controversy regarding the use of antibiotics in the treatment of peri-implantitis, this systematic review and meta-analysis aim to ascertain how beneficial the role of systemic and local antibiotics is in peri-implant surgical therapy, considering the harmful effects that they represent and the abuse of antibiotics in terms of global health. (2) Methods: To determine the therapeutic efficacy of the administration of antibiotics in the surgical treatment of peri-implantitis in terms of probing pocket depth (PPD) and bleeding on probing (BoP), electronic and manual bibliographic searches were carried out in the Embase and PubMed databases, collecting data that related to before and after treatment. (3) Results: The adjunctive use of local antibiotics provides significant improvements in PPD (MD = 1.29; 95% CI: 0.56 to 2.02; p ≤ 0.0006; I² = 0%) when compared with surgical treatment alone. No significant differences were found in the other subgroup; that is, the use of systemic antibiotics did not significantly improve PPD changes in the surgical treatment of peri-implantitis (MD = 0.40; 95% CI: −0.15 to 0.95; p = 0.15; I² = 0). (4) Conclusions: The use of local antibiotics in the surgical treatment of peri-implantitis seems to offer treatment improvements in terms of PPD and BoP, unlike that observed with the use of systemic antibiotics. However, these results should be taken with caution as they also depend on the type of surgical technique used, whether regenerative or resective. More research is needed on this topic to understand the role of local and systemic antibiotics in the treatment of peri-implantitis.

There is no current consensus on the parameters that determine the difficulty of mandibular third molar extraction in terms of the time required, which is essential to prevent complications and optimize the time of the intervention. This study aims to obtain, using the mathematical method of multiple linear regression, an equation that allows estimating the extraction time of a lower third molar according to its complexity, as well as to validate this equation in a sample of external wisdom teeth. Methods: A prospective cohort study on a sample of patients of the Master of Oral Surgery of the University of Seville in which multiple linear regression coefficients were calculated with a subsequent validation study of the results in the sample of patients operated in the Hospital Palmaplanas of Mallorca. Results: The regression line obtained after applying the statistical methodology to the cohort of patients from the University of Seville obtained significant dependent variables such as depth, roots, and odontosection. Once applied to the cohort of patients from the Palmaplanas Hospital in Mallorca, a regression coefficient was obtained between the data received and the estimated 0.770. Conclusions: The formula proposed in this article presents significant validity in the prediction of the surgical time of extraction of the lower third molars included.

Biomechanical bite analysis is essential for understanding occlusal forces and their distribution, especially in the design and validation of dental prostheses. Although the finite element method (FEM) has been widely used to evaluate these forces, the existing models often lack accuracy due to simplified geometries and limited material properties. Methods: A detailed finite element model was developed using Abaqus Standard 2023 software (Dassault Systemes, Vélizy-Villacoublay, France), incorporating scanned 3D geometries of mandibular and maxillary bones. The model included cortical and cancellous bones (Young’s modulus: 5.5 GPa and 13.7 GPa, respectively) and was adjusted to simulate bite forces of 220.7 N based on experimental data. Occlusal forces were evaluated using flexible connectors that replicate molar-to-molar interactions, and the stress state was analyzed in the maxillary and mandibular bones. Results: The FEM model consisted of 1.68 million elements, with mesh sizes of 1–1.5 mm in critical areas. Bite forces on the molars were consistent with clinical trials: first molar (59.3 N), second molar (34.4 N), and third molar (16.7 N). The results showed that the maximum principal stresses in the maxillary bones did not exceed ±5 MPa, validating the robustness of the model for biomechanical predictions. Conclusion: The developed model provides an accurate and validated framework for analyzing the distribution of occlusal forces in intact dentures. This approach allows the evaluation of complex prosthetic configurations and their biomechanical impact, optimizing future designs to reduce clinical complications and improve long-term outcomes. The integration of high-resolution FEM models with clinical data establishes a solid foundation for the development of predictive tools in restorative dentistry.

Background: Numerous studies in the literature have aimed to evaluate the difficulty level of removing third molars. However, most of these studies have focused on the lower third molars, which can lead to complications. There is a lack of a method to determine the complexity of upper third molar extraction. Therefore, this study’s objective was to develop an equation using multiple linear regression to estimate the extraction time of an upper third molar based on its complexity. Methods: This study involved patients enrolled in the Master of Oral Surgery program at the University of Seville. To determine their relationship with surgical time, the researchers analyzed various factors, such as depth, root morphology, and the need for odontosection. They then validated their findings by studying patients treated at Palmaplanas Hospital in Mallorca. Results: The cohort analysis from the University of Seville revealed significant associations between surgical time and the identified factors. A regression equation design was performed to predict the total duration of surgical intervention for wisdom teeth extraction. This equation incorporates several independent variables, represented by Xi, together with a constant term, C, and the corresponding coefficients, Bi, which weight the impact of each variable on the intervention time. The results are as follows: −0.312 (spatial relationship), 0.651 (depth), −0.443 (bone and mucosa integrity), 0.214 (roots), −0.745 (ostectomy), 0.713 (odontosection), and −0.426 (suture). Upon application of the statistical methodology to the Palmaplanas Hospital cohort, a regression coefficient of 0.770 was determined. This indicates a strong correlation between the input data and the estimated surgical time. Conclusions: In conclusion, the proposed formula demonstrates notable validity in predicting the surgical time required to extract upper third molars.