Sacroilliac joint diasthesis from high energy trauma is always complicated with chronic pain and long term morbidity. Open anterior stabilisation with plate allow direct reduction and stabilisation with biomechanically advantages. Here we report on four cases of pelvic injury with sacroiliac joint disruption treated with anterior plate stabilisation through a surgical approach similar to that used for anterior ring fractures.

Animals ; Antibodies ; immunology ; therapeutic use ; Female ; Graves Disease ; immunology ; prevention & control ; Immune Tolerance ; immunology ; Mice ; Mice, Inbred BALB C ; Receptors, Thyrotropin ; immunology

Objectives: Machine learning has been widely used to predict diseases, and it is used to derive impressive knowledge in the healthcare domain. Our objective was to predict in-hospital mortality from hospital-acquired infections in trauma patients on an unbalanced dataset. Methods: Our study was a cross-sectional analysis on trauma patients with hospital-acquired infections who were admitted to Shiraz Trauma Hospital from March 20, 2017, to March 21, 2018. The study data was obtained from the surveillance hospital infection database. The data included sex, age, mechanism of injury, body region injured, severity score, type of intervention, infection day after admission, and microorganism causes of infections. We developed our mortality prediction model by random under-sampling, random over-sampling, clustering (k-mean)-C5.0, SMOTE-C5.0, ADASYN-C5.5, SMOTE-SVM, ADASYN-SVM, SMOTE-ANN, and ADASYN-ANN among hospital-acquired infections in trauma patients. All mortality predictions were conducted by IBM SPSS Modeler 18. Results: We studied 549 individuals with hospital-acquired infections in a trauma hospital in Shiraz during 2017 and 2018. Prediction accuracy before balancing of the dataset was 86.16%. In contrast, the prediction accuracy for the balanced dataset achieved by random under-sampling, random over-sampling, clustering (k-mean)-C5.0, SMOTE-C5.0, ADASYN-C5.5, and SMOTE-SVM was 70.69%, 94.74%, 93.02%, 93.66%, 90.93%, and 100%, respectively. Conclusions Our findings demonstrate that cleaning an unbalanced dataset increases the accuracy of the classification model. Also, predicting mortality by a clustered under-sampling approach was more precise in comparison to random under-sampling and random over-sampling methods.

ABSTRACT Kimura disease (KD) is a rare chronic inflammatory disorder of unknown aetiology that primarily affects the head and neck region with lymph node involvement. Young to middle-aged adult Asian males are predominantly affected. The most common presentation is painless subcutaneous swelling in the head and neck region, while proptosis or orbital involvement is very rarely reported. KD shares some features with other inflammatory and neoplastic disorders, including lymphoma; thus, investigations to confirm the diagnosis should not be delayed. Systemic corticosteroids are commonly used to treat KD and show an excellent response; however, the optimal treatment is still uncertain, and KD has a high recurrence rate. We describe the case of a patient with KD who presented with proptosis and post-auricular swelling, which responded well to oral prednisolone treatment.
Kimura Disease ; Exophthalmos