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Updated: Apr 21, 2026
A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible
Published on: January 28, 2020
Cross-sectional validation of a preoperative multidimensional assessment tool for third molar extraction using
A Sánchez-Torres1, X Arias-Huerta, B Pérez-Iglesias
1Rui Figueiredo Faculty of Medicine and Health Sciences Bellvitge Campus. University of Barcelona C/ Feixa Llarga, s/n; Pavelló Govern, 2nd floor Despatx 2.9. 08907 - L'Hospitalet de Llobregat Barcelona, Spain ruibarbosa@ub.edu.
Background:
This study aimed to assess the correlation between a preoperative difficulty assessment form and surgery time in third molar extractions. Secondary aims included analyzing the relationship between the form's global score and surgical technique, surgeon-perceived difficulty, and postoperative complications. Machine learning models were also explored.
Material And Methods:
A cross-sectional study was conducted on patients requiring a third molar extraction between April 2022 and May 2023, operated by students of the master's degree in Oral Surgery and Implantology (University of Barcelona, Spain). Three previously calibrated investigators completed the preoperative difficulty evaluation form, which included clinical, radiological and surgical variables. Descriptive and bivariate analyses were performed using the Stata/IC 15.1. Machine learning models were applied to predict surgery time and surgeon-perceived difficulty.
Results:
A total of 205 patients, 75 males (36.6%) and 130 females (63.4%), with a mean age of 28.5±14.1 years were included; 49 (23.9%) were upper and 156 (76.1%) lower third molars. The global score of the form was significantly correlated to surgery time (Spearman's rho=0.640; P<0.001), perceived difficulty (Spearman's rho=0.395; P<0.001) and the surgical technique according to Parant's classification (P<0.001). Postoperative complications were associated to higher scores on the difficulty evaluation form (P=0.012).
Conclusions:
The preoperative assessment form is a valid tool for estimating third molar extraction difficulty. Its global score is positively associated with surgery time, perceived difficulty, surgical technique, and postoperative complications. Machine learning models showed better performance at modeling surgery time than surgical difficulty.
