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.

Bruxism is an unconscious, involuntary and sustained motor activity that results in excessive teeth grinding or jaw clenching that could affect patients’ implants and rehabilitations. The aetiology for bruxism remains unknown, but it is known to involve multiple factors. The literature lacks studies on the possible effect of implant morphology on the resistance of the bone-implant osseointegrated interface when bruxism is present. Our objective is to assess the mechanical response of the bone-implant interface in bruxist patients whose implant prostheses are subjected to parafunctional cyclic loading over a simulated period of 10 years. A comparison was carried out between two implant types (M-12 and Astra Tech), and a pattern of bone loss was established considering both the stress state and the cortical bone surface loss as the evaluation criteria. Numerical simulation techniques based on the finite element analysis method were applied in a dynamic analysis of the received forces, together with a constitutive model of bone remodelling that alters the physical properties of the bone. The simulated cortical bone surface loss at the implant neck area was 8.6% greater in the Astra implant than in the M-12 implant. Compared to the M-12 implant, the higher sustained stress observed over time in the Astra implant, together with the greater cortical bone surface loss that occurred at its neck area, may be related to the major probability of failure of the prostheses placed over Astra implants in bruxist patients.

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.