• Sexton McCain posted an update 6 months ago

    familial and social support on stoma adjustment are warranted.A rod passed through the mesenteric window is commonly used during maturation of ileostomies, but evidence for the effectiveness of this procedure is limited. Purpose The aim of this meta-analysis was to determine whether ileostomy rods decrease stoma retraction rates in patients undergoing loop ileostomy (LI). Methods The PubMed, EMBASE, Cochrane Library, MEDLINE via Ovid, Cumulative Index of Nursing and Allied Health Literature, and Web of Science databases were systematically searched for randomized controlled trials (RCT) published in English from 1990 to the present date using the MeSH terms ostomy, rod, and bridge to compare ileostomies with a rod to those without a rod. Study information, patient demographics, characteristics, and stoma retraction rates were abstracted. The primary endpoint, stoma retraction, was defined as the disappearance of normal stomal protrusion to at, or below, skin level. The Mantel-Haenszel method of meta-analysis with odds ratio and 95% confidence interval (OR ) was llow-up. Studies examining the rate of all potential complications in patients who do and do not receive rod placement following IL are needed to help surgeons make evidence-based decisions.Electrical stimulation (E-Stim) involves applying low levels of electrical current. Despite high-level recommendations for E-stim use in many pressure injury (PrI) best practice treatment guidelines, clinicians seldom use E-Stim. Purpose This quasi-experimental design study aimed to determine whether an educational program could improve health care providers’ knowledge and attitudes regarding the use of E-Stim for treating PrIs in community-dwelling individuals with spinal cord injury living in 1 region of Ontario, Canada. Methods An educational intervention based on a university-level continuing education program was developed as part of a multifaceted knowledge mobilization project. Health care providers (eg, nurses, physicians, and allied health professionals) from multiple agencies were invited to participate. The instructional series included 8 online modules on background theory and knowledge and a hands-on workshop that familiarized participants with the equipment necessary to deliver E-Stim. SIS17 ic50 Knowledgectice subscale, attitude increased significantly post-online (t = 6.03, P less then .0001). For the resources subscale, a significant increase was detected after post-workshop (t = 5.23, P less then .001]. Conclusions Online education increased health care providers’ knowledge about E-Stim; however, hands-on workshops were required to change certain attitudes about the use of E-Stim for wound healing. Further research is required to evaluate 1) whether a change in knowledge and attitude scores translates to a practice change for health care providers and 2) the potential importance of ongoing coaching and mentorship for a sustainable change in the clinical setting.Background Qualitative self- or parent-reports used in assessing children’s behavioral disorders are often inconvenient to collect and can be misleading due to missing information, rater biases, and limited validity. A data-driven approach to quantify behavioral disorders could alleviate these concerns. This study proposes a machine learning approach to identify screams in voice recordings that avoids the need to gather large amounts of clinical data for model training. Objective The goal of this study is to evaluate if a machine learning model trained only on publicly available audio data sets could be used to detect screaming sounds in audio streams captured in an at-home setting. Methods Two sets of audio samples were prepared to evaluate the model a subset of the publicly available AudioSet data set and a set of audio data extracted from the TV show Supernanny, which was chosen for its similarity to clinical data. Scream events were manually annotated for the Supernanny data, and existing annotations were refined for the AudioSet data. Audio feature extraction was performed with a convolutional neural network pretrained on AudioSet. A gradient-boosted tree model was trained and cross-validated for scream classification on the AudioSet data and then validated independently on the Supernanny audio. Results On the held-out AudioSet clips, the model achieved a receiver operating characteristic (ROC)-area under the curve (AUC) of 0.86. The same model applied to three full episodes of Supernanny audio achieved an ROC-AUC of 0.95 and an average precision (positive predictive value) of 42% despite screams only making up 1.3% (n=92/7166 seconds) of the total run time. Conclusions These results suggest that a scream-detection model trained with publicly available data could be valuable for monitoring clinical recordings and identifying tantrums as opposed to depending on collecting costly privacy-protected clinical data for model training.Background Due to demographic change and, more recently, coronavirus disease (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients’ vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be due to the lack of user involvement in research and development. Objective This study focused on the satisfaction of ICU staff with current patient monitoring and their suggestions for future improvements. We aimed to identify aspects of monitoring that interrupt patient care, display devices for remote monitoring, use cases for artificial intelligence (AI), and whether ICU staff members are willing to improve their digital literacy or contribute to the improvement of patient monitoring. We further aimed to identify differences in the responses of different professional groups. Methods This survey st digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine. Trial registration ClinicalTrials.gov NCT03514173; https//clinicaltrials.gov/ct2/show/NCT03514173.

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