Plos One
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This paper focuses on the application of machine learning algorithms for predicting spinal abnormalities. As a data preprocessing step, univariate feature selection as a filter based feature selection, and principal component analysis (PCA) as a feature extraction algorithm are considered. A number of machine learning approaches namely support vector machine (SVM), logistic regression (LR), bagging ensemble methods are considered for the diagnosis of spinal abnormality. ⋯ On the other hand, the accuracies for the test dataset for SVM, LR, bagging SVM and bagging LR are the same being 86.96%. However, bagging SVM is the most attractive as it has a higher recall value and a lower miss rate compared to others. Hence, bagging SVM is suitable for the classification of spinal patients when applied on the most five important features of spinal samples.
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Multicenter Study Observational Study
Audit of pre-operative antibiotic prophylaxis usage in elective surgical procedures in two teaching hospitals, Islamabad, Pakistan: An observational cross-sectional study.
An audit of the antibiotic prophylaxis in surgical procedures is the basic area of antimicrobial stewardship programme. The current research aimed to evaluate the adherence-proportion of the pre-operative antibiotic prophylaxis (PAP) practices in common elective surgical procedures. It was an eight-month (January 2017 to August 2017) observational cross-sectional patients' treatment record-based study conducted at two tertiary care teaching hospitals of Islamabad, Pakistan. ⋯ Most of the patients received ceftriaxone, a third-generation cephalosporin that is no longer recommended by the latest international guidelines. The current analysis revealed an alarmingly poor adherence rate with the guidelines in the three elective surgical procedures at both hospitals. To improve the situation, training and awareness programs about the antimicrobial stewardship interventions on the institutional level may be valuable.
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Meta Analysis Comparative Study
Yoga compared to non-exercise or physical therapy exercise on pain, disability, and quality of life for patients with chronic low back pain: A systematic review and meta-analysis of randomized controlled trials.
Chronic low back pain (CLBP) is a common and often disabling musculoskeletal condition. Yoga has been proven to be an effective therapy for chronic low back pain. However, there are still controversies about the effects of yoga at different follow-up periods and compared with other physical therapy exercises. ⋯ This meta-analysis provided evidence from very low to moderate investigating the effectiveness of yoga for chronic low back pain patients at different time points. Yoga might decrease pain from short term to intermediate term and improve functional disability status from short term to long term compared with non-exercise (e.g. usual care, education). Yoga had the same effect on pain and disability as any other exercise or physical therapy. Yoga might not improve the physical and mental quality of life based on the result of a merging.
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Sustained inflation (SI) during chest compression (CC = CC+SI) has been recently shown as an alternative method during cardiopulmonary resuscitation in neonates. However, the optimal peak inflation pressure (PIP) of SI during CC+SI to improve ROSC and hemodynamic recovery is unknown. ⋯ In asphyxiated term newborn piglets resuscitated by CC+SI, the use of different PIPs resulted in similar time to ROSC, but PIP at 30 cmH2O showed a larger VT delivery, lower exhaled CO2 and increased tissue inflammatory markers in the brain.
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To examine the use of Normalisation Process Theory (NPT) to establish if, and in what ways, the AMBER care bundle can be successfully normalised into acute hospital practice, and to identify necessary modifications to optimise its implementation. ⋯ To be successfully normalised, new clinical practices, such as the AMBER care bundle, must be studied within the wider context in which they operate. NPT can be used to the aid identification of practical strategies to assist in normalisation of complex interventions where the focus of care is on clinical uncertainty in acute hospital settings.