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.

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.

Background: Implant-supported prosthetic rehabilitation is widely used to restore function in edentulous patients. Although most implant finite element studies prescribe occlusal forces as input loads and evaluate the resulting stresses, the effect of implant-supported rehabilitation on bite-force distribution remains insufficiently characterized. This study evaluated how three clinically relevant implant-supported rehabilitation configurations modify occlusal force distribution relative to the physiological reference pattern. Methods: A three-dimensional finite element model of the head and neck, calibrated for physiological symmetric bite conditions, was developed using high-resolution anatomical reconstructions of the maxilla, mandible, zygomatic bones, and dentition. Three clinical configurations were analyzed: three conventional implants, one conventional plus one zygomatic implant, and two zygomatic implants. The model was calibrated to reproduce a total physiological occlusal force of 220.7 N, and the resulting force distribution was compared with published data for intact dentition. Results: All implant-supported configurations altered the physiological occlusal force pattern, concentrating between 71% and 78% of the total bite force on the first molars adjacent to the posterior implants (46–36). In contrast, the three implant configurations showed only minor differences in overall stress distribution in the maxillary and zygomatic bones. Conclusions: Implant-supported rehabilitation substantially modified the physiological bite-force distribution in all three simulated configurations, whereas only limited differences in the overall bone stress pattern were observed among the configurations. These differences should be interpreted at the configuration level, since the present study cannot separate the independent effects of implant number, position, and type design. In contrast, conventional or zygomatic implants have.