The purpose of this study was to investigate the prognostic value of prognostic nutritional index (PNI) in oral squamous cell carcinoma (OSCC) patients and to undertake a comparative evaluation of the prognostic value of comparing PNI, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR) in terms of prognostic utility. A retrospective study was conducted involving 203 consecutive patients with OSCC who were treated with radical surgery with curative intent. The PNI and systemic inflammatory response were developed, and their prognostic utility was evaluated. Kaplan-Meier curve analysis and log-rank testing showed that PNI (P less then 0.001), NLR (P=0.011), PLR (P=0.013), and LMR (P=0.014) were significantly associated with overall survival.  https://www.selleckchem.com/products/monomethyl-auristatin-e-mmae.html  Multivariate analysis identified PNI as an independent prognostic factor for OSCC patients (P=0.029). In time-dependent receiver operating characteristic curve analysis, PNI was continuously superior to that of NLR, PLR, and LMR. In conclusion, this study suggested that PNI offered an independent prognostic biomarker in OSCC patients undergoing radical surgery. However, this study was small and retrospective, thus further investigations are needed to clarify the utility of PNI for tailor-made treatments in clinical settings.The objective of this study was to describe the authors' long-term experience with the management of odontogenic keratocysts (OKCs). All OKC cases treated at the study centre between 1999 and 2015, with a minimum of 5 years of follow-up by December 2019, were reviewed retrospectively. Operative procedures including decompression/marsupialization, enucleation (E), E+Carnoy's solution (CS), E+CS+peripheral ostectomy (PO), and resection were assessed for complete resolution, partial resolution, and recurrence rates. In the parakeratinized non-syndromic group, E+CS+PO resulted in the lowest recurrence rate among the minimally invasive procedures (4.3%), while enucleation resulted in the highest rate (60%). Regarding the other modalities, recurrence was 12.5% for decompression, 11.5% for marsupialization, 16.7% for E+CS, 26.7% for E+PO, and 0% for resection. In the syndromic group, marsupialization resulted in a significantly higher recurrence (23.1%), while E+CS+PO cases showed no recurrence. No recurrence was observed in the orthokeratinized group patients treated with marsupialization or with E+CS. Based on clinico-radiographic features and observed results, it is concluded that OKC, although having a high recurrence rate, is a benign lesion and responds well to conservative procedures in most cases. Radical procedures should be reserved for unresponsive lesions and those with extensive tissue destruction.Similar to the experiences of other radiology practices, our radiology staff members felt that scored peer review identified few errors/learning opportunities while undermining team collegiality. They desired a more effective way to promote team collegiality and foster lifelong learning. We describe the steps our department took to transition from a peer review system to a peer learning program.
  Dislocation is a common complication following total hip arthroplasty (THA), and accounts for a high percentage of subsequent revisions. The purpose of this study is to illustrate the potential of a convolutional neural network model to assess the risk of hip dislocation based on postoperative anteroposterior pelvis radiographs.
 
  We retrospectively evaluated radiographs for a cohort of 13,970 primary THAs with 374 dislocations over 5 years of follow-up. Overall, 1490 radiographs from dislocated and 91,094 from non-dislocated THAs were included in the analysis. A convolutional neural network object detection model (YOLO-V3) was trained to crop the images by centering on the femoral head. A ResNet18 classifier was trained to predict subsequent hip dislocation from the cropped imaging. The ResNet18 classifier was initialized with ImageNet weights and trained using FastAI (V1.0) running on PyTorch. The training was run for 15 epochs using 10-fold cross validation, data oversampling, and augmentation.
 
  The hip dislocation classifier achieved the following mean performance (standard deviation) accuracy= 49.5 (4.1%), sensitivity= 89.0 (2.2%), specificity= 48.8 (4.2%), positive predictive value= 3.3 (0.3%), negative predictive value= 99.5 (0.1%), and area under the receiver operating characteristic curve= 76.7 (3.6%). Saliency maps demonstrated that the model placed the greatest emphasis on the femoral head and acetabular component.
 
  Existing prediction methods fail to identify patients at high risk of dislocation following THA. Our radiographic classifier model has high sensitivity and negative predictive value, and can be combined with clinical risk factor information for rapid assessment of risk for dislocation following THA. The model further suggests radiographic locations which may be important in understanding the etiology of prosthesis dislocation. Importantly, our model is an illustration of the potential of automated imaging artificial intelligence models in orthopedics.
 
  Level III.
 Level III.
  The purpose of our study is to assess which patient-related and caregiver-related factors are predictive of caregiver strain and assistance when caring for total hip and knee arthroplasty (THA and TKA) patients within 2 weeks after surgery.
 
  We conducted a prospective study of caregivers of participants enrolled in 2 randomized trials. Caregivers provided demographics and completed the Caregiver Strain Index and Caregiver Assistance Scale pre-surgery and post-surgery. We performed backwards stepwise regression with mixed-effects negative binomial models to investigate predictors of caregiver strain and assistance for THA and TKA caregivers.
 
  Three hundred six caregiver/patient pairs were included. Our models of caregiver strain found Caregiver Assistance Scale scores and patient age to be predictive for all caregivers. We also found caregiver gender and smoking status to be predictive for THA caregivers and caregiver age to be predictive for TKA caregivers. Our models of assistance provided by caregivers found time (post-surgery vs pre-surgery) was predictive for all caregivers.