Mcleodconradsen0328

tion number NCT03533205.

Clinical trials registration number NCT03533205.

Formal patient complaints are associated with increased malpractice litigation and can have adverse occupational consequences for surgeons. selleck chemicals llc Our purpose was to define and categorize patient complaints within an academic pediatric orthopaedic surgery practice over a 10-year period. We further aimed to define risk factors associated with patient complaints.

We reviewed all complaints within our institution's patient advocacy service filed on behalf of a patient against 4 pediatric orthopaedic surgeons over a 10-year period. Complaints were categorized using the Patient Complaint Analysis System. A control group of all patients seen by the surgeons during the study period was created. We compared baseline demographics between the patients with a complaint and the control group and compared complaint rates between the surgeons. Any malpractice events (lawsuits and claims) associated with the surgeons were obtained. We queried our institutional MIDAS reporting system (which allows for anonymous reporting of potassociated with increased complaints. Understanding patient complaints rates and types may allow surgeons to target areas for improvement and decrease exposure to malpractice litigation.

Level II-prognostic.

Level II-prognostic.

Telemedicine, or telehealth, is broadly defined as the use of technology to deliver health care or health education at distance. Synchronous communication telemedicine, which involves the use of real time audio and video between patient and provider, is a developing field and its use in the pediatric orthopaedic population is not well defined. To better understand the existing use of and challenges posed by telemedicine for pediatric orthopaedic providers, the Pediatric Orthopaedic Society of North America (POSNA) practice management committee conducted a survey of current POSNA members.

A 33-question survey was created by the POSNA Practice Management Committee, piloted among committee members and approved by the POSNA Evidence Based Practice Committee. A total of 167 responses were complete (75% complete response rate). Telemedicine use questions referred to the use of synchronous communication telemedicine.

A total of 50% reported being "moderately" or "not really" familiar with telemedicine. Sixty py of survey participants utilize synchronous communication telemedicine. There is a desire for telemedicine training and best billing practices education. Further study to evaluate the limitations and efficacy of telemedicine in the pediatric orthopaedic population will be necessary as its use increases.

Surgical navigation improves pedicle screw insertion accuracy and reliability. Robotic-assisted spinal surgery and screw placement has not been fully assessed in pediatric patients with spine deformity undergoing posterior spinal fusion. The purpose of this study was to describe the learning curve for robotically assisted pedicle screw placement in pediatric patients.

A retrospective review on a consecutive series of the first 19 pediatric patients who underwent posterior spinal fusion by a single surgeon using robotic navigation was performed. Demographics, curve parameters, pedicle diameter, vertebral rotation, and additional outcome measures were recorded. Screw position was assessed with calibrated intraoperative 3-dimensional fluoroscopic images. All complications of planned and placed robotically placed screws were recorded.

A total of 194 left-sided screws were planned as robot-assisted. One hundred sixty-eight of the robotically planned screws (86.6%) were placed with robot assistance; 29 robotining lead to an improved understanding of surgical planning, skive, and soft-tissue pressure on the end-effector all of which can impact accuracy of robotically assisted pedicle screw placement.

Case Series, IV.

Case Series, IV.

Artificial intelligence has become popular in medical applications, specifically as a clinical support tool for computer-aided diagnosis. These tools are typically employed on medical data (i.e., image, molecular data, clinical variables, etc.) and used the statistical and machine-learning methods to measure the model performance. In this review, we summarized and discussed the most recent radiomic pipeline used for clinical analysis.

Currently, limited management of cancers benefits from artificial intelligence, mostly related to a computer-aided diagnosis that avoids a biopsy analysis that presents additional risks and costs. Most artificial intelligence tools are based on imaging features, known as radiomic analysis that can be refined into predictive models in noninvasively acquired imaging data. This review explores the progress of artificial intelligence-based radiomic tools for clinical applications with a brief description of necessary technical steps. Explaining new radiomic approaches based on deep-learning techniques will explain how the new radiomic models (deep radiomic analysis) can benefit from deep convolutional neural networks and be applied on limited data sets.

To consider the radiomic algorithms, further investigations are recommended to involve deep learning in radiomic models with additional validation steps on various cancer types.

To consider the radiomic algorithms, further investigations are recommended to involve deep learning in radiomic models with additional validation steps on various cancer types.

Chronic venous disease is a debilitating condition involving great saphenous vein (GSV) incompetence.

To investigate the efficacy and effectiveness of cyanoacrylate embolization (CAE) versus radiofrequency ablation (RFA) in patients with incompetent GSVs.

PubMed, Embase, and the Cochrane library were searched. The primary outcomes were the Venous Clinical Severity Score (VCSS), Aberdeen Varicose Vein Questionnaire (AVVQ), closure rate, and visual analog scale (VAS) for pain.

This meta-analysis included 378 and 590 patients who underwent CAE and RFA, respectively. Cyanoacrylate embolization was comparable with RFA in VCSS (weighted mean difference [WMD] = -0.03, 95% confidence interval [CI] -0.18 to 0.12, p = .686), AVVQ (WMD = -0.08, 95% CI -0.38 to 0.21, p = .570), closure rate (odds ratio [OR] = 0.61, 95% CI 0.18-2.01, p = .414), and VAS (standardized mean difference [SMD] = 0.24, 95% CI -0.59 to 1.06, p = .523). There were no significant differences between CAE and RFA regarding the occurrence of phlebitis (OR = 1.