Pain management in adults during long-bone fracture reduction in the Gaza Strip: a clinical audit.

BACKGROUND: Ongoing protests in Gaza have led to numerous injuries, including long-bone fractures. We investigated assessment of pain severity and strategies for pain management in the emergency department. As no local guidelines exist, delivered care was compared with the guidance of the UK National Institute for Health and Care Excellence (NICE). METHODS: A clinical audit was conducted at the emergency department in Dar Al Shifa Medical Complex, Gaza, among patients who attended the emergency department with acute long-bone fractures between April 15 and July 15, 2018. Data were collected on pain assessment methods and strategies for pain management and analgesia administration. Ethics approval was obtained from the Palestinian Ministry of Health. The purpose of the audit was explained to patients and their written consent was obtained before inclusion. FINDINGS: Of 79 patients invited to participate, 50 gave consent. 25 patients (50%) were aged 16-24 years, 20 (40%) 25-64 years, and five (10%) 65 years and older. Structured pain assessments were performed in only three patients (6%). No analgesia was administered to patients in the emergency department, except for two patients (4%) received infiltration of lidocaine as analgesia for haematoma. INTERPRETATION: No local guidelines for acute pain management in emergency departments exist in the Gaza Strip. Clinical practice showed no adherence to international standards, such as the NICE guidelines for pain management. Factors contributing to such poor management might be large numbers of patients presenting at the time during conflict and protests and that only one room was available in the Shifa' Medical Complex emergency department for examining, assessment, prescribing, and cast application. A limitation of the study is the small sample size is relatively small, but strengths were the 3-month period and prospective enrolment in the emergency department. FUNDING: None.

A robust deep learning model for the classification of dental implant brands.

OBJECTIVE: In cases where the brands of implants are not known, treatment options can be significantly limited in potential complications arising from implant procedures. This research aims to explore the application of deep learning techniques for the classification of dental implant systems using panoramic radiographs. The primary objective is to assess the superiority of the proposed model in achieving accurate and efficient dental implant classification. MATERIAL AND METHODS: A comprehensive analysis was conducted using a diverse set of 25 convolutional neural network (CNN) models, including popular architectures such as VGG16, ResNet-50, EfficientNet, and ConvNeXt. The dataset of 1258 panoramic radiographs from patients who underwent implant treatment at faculty of dentistry was utilized for training and evaluation. Six different dental implant systems were employed as prototypes for the classification task. The precision, recall, F1 score, and support scores for each class have included in the classification accuracy report to ensure accurate and reliable results from the model. RESULTS: The experimental results demonstrate that the proposed model consistently outperformed the other evaluated CNN architectures in terms of accuracy, precision, recall, and F1-score. With an impressive accuracy of 95.74 % and high precision and recall rates, the ConvNeXt model showcased its superiority in accurately classifying dental implant systems. Notably, the model's performance was achieved with a relatively smaller number of parameters, indicating its efficiency and speed during inference. CONCLUSION: The findings highlight the effectiveness of deep learning techniques, particularly the proposed model, in accurately classifying dental implant systems from panoramic radiographs.

A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System.

This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system's potential in clinical practices and to offer a strategic framework for improving diagnosis and treatment processes in implantology. This study employed a total of 28 different deep learning models, including 18 convolutional neural network (CNN) models (VGG, ResNet, DenseNet, EfficientNet, RegNet, ConvNeXt) and 10 vision transformer models (Swin and Vision Transformer). The dataset comprises 1258 panoramic radiographs from patients who received implant treatments at Erciyes University Faculty of Dentistry between 2012 and 2023. It is utilized for the training and evaluation process of deep learning models and consists of prototypes from six different implant systems provided by six manufacturers. The deep learning-based dental implant system provided high classification accuracy for different dental implant brands using deep learning models. Furthermore, among all the architectures evaluated, the small model of the ConvNeXt architecture achieved an impressive accuracy rate of 94.2%, demonstrating a high level of classification success.This study emphasizes the effectiveness of deep learning-based systems in achieving high classification accuracy in dental implant types. These findings pave the way for integrating advanced deep learning tools into clinical practice, promising significant improvements in patient care and treatment outcomes.

Pyeloplasty for a Child With a Retrocaval Ureter: A Case Report.

Retrocaval ureter, also known as a circumcaval ureter or pre-ureteral vena cava, is a rare entity in which the patient's ureter passes backward to the inferior vena cava (IVC). The IVC compresses the upper portion of the ureter resulting in varying degrees of hydroureteronephrosis. We report a case of a child with retrocaval ureter malformation that was incidentally diagnosed while investigating for intermittent right renal colic, which was successfully treated by subcostal pyeloplasty. This case highlights the importance of radiographic imaging to diagnose retrocaval ureter, with computed tomography as a definitive method, but other modalities, including magnetic resonance imaging, ultrasound, and pyelography, are also helpful.

A Review of Diagnosis and Management: Persistent Cloaca Treated by a Posterior Sagittal Approach With a Normal Functional Outcome.

Cloacal malformation (CM) is a severe, complex, and extremely rare category of anorectal and urogenital tract malformations. Prenatal diagnosis is illusory and vague; therefore, magnetic resonance imaging (MRI) is the most effective test point toward an accurate diagnosis. Thus, careful investigation and evaluation are mandatory since they could be associated with syndromes and other anomalies, including urogenital tract, vertebral, and cord abnormalities. Despite the severity and complexity of the deformity, CM cases are curable, not desperate, and can have an excellent prognosis with great surgical correction. However, managing persistent cloaca necessitates a careful assessment because corrective surgeries require inclusive surgical planning, multidisciplinary, expert, and highly specialized medical center. In surgically repaired malformations, fecal and urinary incontinence has been a major issue, which was resolved when Dr. Pena Alberto suggested safer dissection and less harmful techniques for neurovascular structures and great functional corrected anomaly to ensure fertility and less incontinence. For improved results and prognosis on quality of life, patients should be scheduled for extended bowel training along with the clinical evaluation follow-up. In this article, we present a case successfully treated with the posterior sagittal approach, Pena operation, and anorecto-vagino-urethroplasty with feminizing clitoroplasty and highlight the value and impact of prenatal evaluation, diagnosis, and management. The rarity of the case and excellent results, including fair to normal bowel and urinary control, prompted us to report it and assert the significance of assessment, surgical management and technique, challenges, postoperative bowel training, and clinical investigation and examination.