A secondary anterior open bite may develop after a mandibular condyle fracture, particularly when an initial conservative treatment fails to restore vertical ramus height or joint function. Herein, we describe a patient with unstable preexisting occlusion who developed a persistent anterior open bite after bilateral condylar neck fractures were managed conservatively. Bilateral condylar reconstruction was planned through virtual surgical simulation and executed with patient-specific guides to accurately reproduce the intended mandibular position. Partial replacement of the mandibular condyle was selected because the soft tissues within the mandibular fossae, including the discs, remained intact and demonstrated functional mobility over the articulating surfaces. Postoperative evaluation revealed close agreement between the planned and achieved mandibular positions, with less than two millimeters of deviation, and stable occlusion was maintained during long-term follow-up. This case illustrates the potential of digitally assisted planning to achieve predictable outcomes in patients with complex secondary deformities.

Purpose: Li-Fraumeni syndrome (LFS) is a hereditary disorder caused by germline mutation in TP53. Owing to the rarity of LFS, data on its clinical features are limited. This study aimed to evaluate the clinical characteristics and prognosis of Korean patients with LFS. Materials and Methods: Patients who underwent genetic counseling and confirmed with germline TP53 mutation in the National Cancer Center in Korea between 2011 and 2022 were retrospectively reviewed. Data on family history with pedigree, types of mutation, clinical features, and prognosis were collected. Results: Fourteen patients with LFS were included in this study. The median age at diagnosis of the first tumor was 32 years. Missense and nonsense mutations were observed in 13 and one patients, respectively. The repeated mutations were p.Arg273His, p.Ala138Val, and pPro190Leu. The sister with breast cancer harbored the same mutation of p.Ala138Val. Seven patients had multiple primary cancers. Breast cancer was most frequently observed, and other types of tumor included sarcoma, thyroid cancer, pancreatic cancer, brain tumor, adrenocortical carcinoma, ovarian cancer, endometrial cancer, colon cancer, vaginal cancer, skin cancer, and leukemia. The median follow-up period was 51.5 months. Two and four patients showed local recurrence and distant metastasis, respectively. Two patients died of leukemia and pancreatic cancer 3 and 23 months after diagnosis, respectively. Conclusion This study provides information on different characteristics of patients with LFS, including types of mutation, types of cancer, and prognostic outcomes. For more appropriate management of these patients, proper genetic screening and multidisciplinary discussion are required.

Purpose: The purpose of this study was to evaluate the accuracy of four prediction models in adult burn patients.M ethods: This retrospective study was conducted on 696 adult burn patients who were treated at burn intensive care unit (BICU) of Hallym University Hangang Sacred Heart Hospital from January 2017 to December 2019. The models are ABSI, APACHE IV, rBaux and Hangang score. Results: The discrimination of each prediction model was analyzed as AUC of ROC curve. AUC value was the highest with Hangang score of 0.931 (0.908∼0.954), followed by rBaux 0.896 (0.867∼0.924), ABSI 0.883 (0.853∼0.913) and APACHE IV 0.851 (0.818∼0.884). Conclusion The results of evaluating the accuracy of the four models, Hangang score showed the highest prediction. But it is necessary to apply the appropriate prediction model according to characteristics of the burn center.

Purpose: The purpose of this study was to evaluate the accuracy of four prediction models in adult burn patients.M ethods: This retrospective study was conducted on 696 adult burn patients who were treated at burn intensive care unit (BICU) of Hallym University Hangang Sacred Heart Hospital from January 2017 to December 2019. The models are ABSI, APACHE IV, rBaux and Hangang score. Results: The discrimination of each prediction model was analyzed as AUC of ROC curve. AUC value was the highest with Hangang score of 0.931 (0.908∼0.954), followed by rBaux 0.896 (0.867∼0.924), ABSI 0.883 (0.853∼0.913) and APACHE IV 0.851 (0.818∼0.884). Conclusion The results of evaluating the accuracy of the four models, Hangang score showed the highest prediction. But it is necessary to apply the appropriate prediction model according to characteristics of the burn center.