Impact of the curriculum system on the quality of stomatology graduates: a multivariable analysis.

BACKGROUND: The curriculum system is a central component in achieving the goals and specifications of talent training schemes. However, problems and difficulties exist in curriculum provision due to a lack of curriculum system design logic. This study aimed to investigate the correlation between the university curriculum system and graduate quality and to reveal the design logic of the curriculum system. METHODS: A total of 699 stomatology graduates from a university in Zhejiang were selected as research subjects from 2015 to 2022. The students' curriculum system and graduate quality data were collected and classified. The graduate quality information contained (1) the National Board Dental Examination (NBDE) pass rate and score, (2) the postgraduate entrance rate and destination, and (3) the employment rate and work institution. Regression analysis was also conducted to assess the correlation between the curriculum system and graduate quality. RESULTS: Regression analysis revealed significant associations between general education, specialization, and stomatology X courses and NBDE score, postgraduate entrance rate and destination, and work institution. All courses except public elective courses had significant impacts on NBDE score. General basic, medical English, and employment guidance courses significantly influenced the postgraduate entrance rate and destination. Restricted elective and public elective courses had significant effects on employment rate and work institution. CONCLUSIONS: Increasing the quality of specialized and stomatology X courses in the curriculum system is beneficial for deepening graduates' expertise and enhancing their education. Moreover, English courses are suggested to be offered in the early stage to lay a better language foundation.

Palatal root endodontic microsurgery in maxillary molars using the palatal approach: a case study.

BACKGROUND: In clinical practice, the buccal approach is typically the primary choice for endodontic microsurgery. Owing to the thickness of the buccal bone plate, the distance between the buccal bone plate and palatal lesion location, and soft tissue traction, the palatal approach may be more suitable for microsurgery for apical periodontitis of the palatal roots of the maxillary molars. However, the length of the palatal root, location of the greater palatine artery (GPA) and foramen (GPF), and surgical field of observation make palatal surgery challenging. CASE PRESENTATION: With the aid of Cone-beam computed tomography imaging, the palatal approach was successfully applied in nine cases of endodontic microsurgery of the palatal root of a maxillary molar with a periapical lesion in Hangzhou Stomatology Hospital from January to December 2022. CONCLUSIONS: Complete healing was assessed based on clinical symptoms and radiographic images at the 3- and 24-month follow-up visits in all nine cases. Several tips have been proposed including surgical positioning, incision design, palate flap modification, bone removal, and root-end resection.

Road-Network-Map-Assisted Vehicle Positioning Based on Pose Graph Optimization.

Satellite signals are easily lost in urban areas, which causes difficulty in vehicles being located with high precision. Visual odometry has been increasingly applied in navigation systems to solve this problem. However, visual odometry relies on dead-reckoning technology, where a slight positioning error can accumulate over time, resulting in a catastrophic positioning error. Thus, this paper proposes a road-network-map-assisted vehicle positioning method based on the theory of pose graph optimization. This method takes the dead-reckoning result of visual odometry as the input and introduces constraints from the point-line form road network map to suppress the accumulated error and improve vehicle positioning accuracy. We design an optimization and prediction model, and the original trajectory of visual odometry is optimized to obtain the corrected trajectory by introducing constraints from map correction points. The vehicle positioning result at the next moment is predicted based on the latest output of the visual odometry and corrected trajectory. The experiments carried out on the KITTI and campus datasets demonstrate the superiority of the proposed method, which can provide stable and accurate vehicle position estimation in real-time, and has higher positioning accuracy than similar map-assisted methods.