Asahi University

Surgical site infection (SSI) is a serious complication of artificial arthroplasty. Its relationship with dental infections and the necessity of preoperative dental screening remains debated. This study examined the relationship between oral condition and risk of SSI, along with preoperative tooth extraction effect, in patients undergoing artificial arthroplasty.

Patients undergoing artificial hip or knee arthroplasty between April 2018 and September 2021 were retrospectively examined for age, sex, surgical site, surgical procedure, surgical time, preoperative hemoglobin level, white blood cell count, albumin level, creatinine level, remaining teeth, apical lesions ≥3 mm, periodontal pockets ≥4 mm, root fractures, residual roots, local infection symptoms in the oral cavity, preoperative or postoperative tooth extraction, and SSI occurrence. Tooth extraction effects on SSI incidence were analyzed using propensity score matching.

The study included 3950 patients from 30 facilities. SSI occurred in 79 patients (2.0 %). Multivariate analysis identified surgery time and oral infection symptoms as significant risk factors. SSI occurred in 75 of 3793 patients without preoperative tooth extraction (2.0 %) and in 4 of 157 patients with tooth extraction (2.6 %), with no significant difference. However, in 292 propensity-matched patients, SSI incidences were 2.7 % and 6.2 % with and without preoperative extraction, respectively, indicating that preoperative infectious tooth removal may reduce SSI risk. Conversely, SSI occurred in three of seven patients (42.9 %) with tooth extraction within 60 days post-surgery, highlighting its risks.

Oral infections increase SSI risk after artificial arthroplasty. Preoperative tooth extraction is recommended to reduce this risk.

The quality of care in care facilities for older adults varies according to the employees' satisfaction level. This study aimed to clarify the relationship between the level of support from supervisors and colleagues and job satisfaction among nurses working in care facilities for older persons. Using a cross-sectional design, we analyzed the responses of 295 nurses in long-term care facilities in Japan to a questionnaire survey. The surveyed items included the nurses' satisfaction with their current job (the dependent variable), and the degree of support from supervisors and colleagues. Of the 295 nurses, 166 (56.3%) were satisfied with their nursing jobs. Additionally, 125 (42.4%) nurses had low support from both supervisors and colleagues, 110 (37.3%) had high support from either supervisors or colleagues, and 60 (20.3%) had high support from both. There was a positive association between support from supervisors and nurses' satisfaction. Furthermore, based on the criterion of low support from supervisors and colleagues, high support from either or both was significantly associated with nurses' satisfaction (an odds ratio of 2.04 in the case of support from either and 4.02 in the case of support from both). These results suggest that there should be strategic hiring and training of managers who can support staff, as well as the strengthening of teamwork.

The purpose of this study is to automatically extract the information necessary for chart recording from panoramic radiographs, to reduce the workload for dentists.

Using 1,085 dental panoramic radiographs (994 of permanent dentition and 91 of mixed dentition) taken at 10 facilities, we conducted tooth detection, numbering, and condition classification. Tooth condition was defined into five classes: natural, partial restoration, prosthetic crown, implant, and pontic. First, the YOLOv7 model was used to simultaneously detect 10 classes of deciduous teeth, 16 classes of permanent teeth, and four classes of tooth condition (excluding natural). We applied rule-based post-processing to the detected objects. Precision, Recall, and F1-score were used to evaluate our method, with an IoU (Intersection over Union) threshold set at 0.5.

We achieved Precision, Recall, and F1-score of 98.51%, 98.38%, and 98.45%, respectively, in tooth numbering. In tooth condition classification, the average F1-score across the 5 classes was 95.47%.

Our method, which detects and classifies the tooth numbers of permanent and deciduous teeth and their tooth condition simultaneously, is expected to contribute to reducing the workload of dentists and improving accuracy.