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Objectives: The main goal of presurgical evaluation in drug-resistant focal epilepsy is to identify a seizure onset zone (SOZ). Of the noninvasive, yet resource-intensive tests available, ictal single-photon emission computed tomography (SPECT) aids SOZ localization by measuring focal increases in blood flow within the SOZ via intravenous peri-ictal radionuclide administration. Recent studies indicate that geographic and center-specific factors impact utilization of these diagnostic procedures.

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Controlling preoperative anxiety is necessary in pediatric patients to avoid adverse effects such as emergence delirium, behavioral problems, post-traumatic stress disorder, anxiety prior to future procedures, and increased analgesic doses in the recovery room. Some patients, especially ones with behavioral issues, have a difficult time arriving at the hospital. Medications given at home can be helpful.

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Background: Fontan surgery is performed at 2-4 years of age and is the third planned surgical intervention for children with a univentricular heart. Major challenges for children and parents after Fontan include (a) psychological distress, (b) prolonged pleural drainage, and (c) the need for postoperative anticoagulation. The aim of this study was to evaluate a pre-Fontan video-based intervention for parents to address these challenges.

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Background: New health care professionals, such as the physician associate or assistant (PA), have expanded the ability of health systems to meet the needs of the population in both primary and secondary health care settings. Although PAs are widely deployed in the emergency department (ED), their role in the ED has not previously been formally described. This systematic scoping review synthesizes and critically analyzes existing literature on the impact and perception of the role of PAs working in the ED.

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Preadmission assessment of extended length of hospital stay with RFECV-ETC and hospital-specific data.

Eur J Med Res

July 2022

School of Health Sciences, Department of Health and Biostatistics, Swinburne University, John Street Hawthorn, Victoria, 3122, Australia.

Background: Patients who exceed their expected length of stay in the hospital come at a cost to stakeholders in the healthcare sector as bed spaces are limited for new patients, nosocomial infections increase and the outcome for many patients is hampered due to multimorbidity after hospitalization.

Objectives: This paper develops a technique for predicting Extended Length of Hospital Stay (ELOHS) at preadmission and their risk factors using hospital data.

Methods: A total of 91,468 records of patient's hospital information from a private acute teaching hospital were used for developing a machine learning algorithm relaying on Recursive Feature Elimination with Cross-Validation and Extra Tree Classifier (RFECV-ETC).

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