Underwoodlim1504

Within the 10% sample, approximately 50% of all patients were from a surgical department, all of which were missing both electronic and paper discharge summaries.

Our study describes indicators of missing electronic DS. The DS impacts interprofessional communication, patient outcomes, and data quality. Therefore, the implications of an incomplete DS are widespread. Our findings will caution future researchers using EMR data about the potential for incomplete data, particularly for patients who are surgical, middle aged, and have fewer comorbidities.

Our study describes indicators of missing electronic DS. The DS impacts interprofessional communication, patient outcomes, and data quality. Therefore, the implications of an incomplete DS are widespread. Our findings will caution future researchers using EMR data about the potential for incomplete data, particularly for patients who are surgical, middle aged, and have fewer comorbidities.

Chronic disease (CD) is a leading cause of population mortality, illness and disability. Identification of CD using administrative data is increasingly used and may have utility in monitoring population health. Pharmaceutical administrative data using World Health Organization, Anatomic Therapeutic Chemical Codification (ATC) assigned to prescribed medicines may offer an improved method to define persons with certain CD and enable the calculation of population prevalence.

To assess the feasibility of Australian Pharmaceutical Benefits Scheme (PBS) dispensing data, to provide realistic measures of chronic disease prevalence using ATC codification, and compare values with international data using similar ATC methods and Australian community surveys.

Twenty-two chronic diseases were identified using World Health Organization (WHO) formulated ATC codes assigned to treatments received and recorded in a PBS database. Distinct treatment episodes prescribed to individuals were counted annually for prevalence esistrative pharmaceutical dispensing data may provide an alternative perspective on population health and a useful resource to estimate the prevalence of a number of chronic diseases within the Australian population.

There is growing evidence regarding the imaging findings of coronavirus disease 2019 (COVID-19) in chest X-rays and computed tomography scans; however, their availability during this pandemic outbreak might be compromised. Currently, the role of point-of-care ultrasonography (POCUS) has yet to be explored.

To describe the POCUS findings of COVID-19 in patients with the disease admitted to the emergency department (ED), correlating them with vital signs, laboratory and radiologic results, therapeutic decisions, and the prognosis.

Prospective study performed in the ED of 2 academic hospitals. selleck chemicals Patients with highly suspected or confirmed COVID-19 underwent a lung ultrasonography (lung POCUS), focused cardiac ultrasound (FOCUS), and inferior vena cava (IVC) exam.

Between March and April 2020, 96 patients were enrolled. The mean age was 68.2 years (SD 17.5). The most common findings in the lung POCUS were an irregular pleural line (63.2%), bilateral confluence (55.2%), and isolated B-lines (53.1%), which were associated with a positive RT-PCR (odds ratio 4.327; 95% CI 1.216-15.401;

<.001), and correlated with IL-6 levels (rho=0.622;

=.002). The IVC negatively correlated with levels of expiratory pO

(rho=-0.539;

=.014) and inspiratory pO

(rho=-0.527;

=0.017), and expiratory diameter positively correlated with troponin I (rho=0.509;

=.03). After the POCUS exam, almost 20% of the patients had an associated condition that required a change in their treatment or management.

POCUS parameters have the potential to impact the diagnosis, management, and prognosis of patients with confirmed or suspected COVID-19.

POCUS parameters have the potential to impact the diagnosis, management, and prognosis of patients with confirmed or suspected COVID-19.The COVID-19 pandemic represents one of the greatest global crises in modern history. In addition to recession and high unemployment, agencies such as the Centers for Disease Control and Prevention warn that stressors associated with a pandemic can cause increased strains, including difficulty concentrating, anxiety, and decreased mental health (CDC, 2020). Two general frameworks that explain these stressor-strain relationships over time include stress-reaction and adaptation models. Stress-reaction models suggest that stressors, such as heightened job demands due to the pandemic, accumulate over time and thus prolonged exposure to these stressors results in both immediate and long-term strain; conversely, adaptation models suggest that people adapt to stressors over time, such that strains produced by ongoing stressors tend to dissipate. After controlling for county-level COVID-19 cases, we found that (a) workers in general exhibited decreasing cognitive weariness and psychological symptoms over time, providing support for the adaptation model; (b) on-site workers experienced increasing physical fatigue over time, supporting the stress-reaction model among those workers; and (c) engaging in recovery behaviors was associated with improvements in cognitive weariness and psychological symptoms for all workers. We also found that our Time 1 outcomes were significantly different than pre-pandemic norms, such that our participants displayed lower initial levels of job-related burnout and higher initial levels of psychological symptoms than pre-pandemic norms. Furthermore, supplemental qualitative data support our quantitative findings for recovery behaviors. These findings have important implications for understanding workers' responses to the pandemic and they can help inform organizational practice.

Intrauterine growth restriction (IUGR) is one of the most common causes of stillbirths. The objective of this study is to develop a machine learning model that will be able to accurately and consistently predict whether the estimated fetal weight (EFW) will be below the 10th percentile at 34+0-37 + 6 week's gestation stage, by using data collected at 20 + 0 to 23 + 6 weeks gestation.

Recruitment for the prospective Safe Passage Study (SPS) was done over 7.5 years (2007-2015). An essential part of the fetal assessment was the non-invasive transabdominal recording of the maternal and fetal electrocardiograms as well as the performance of an ultrasound examination for Doppler flow velocity waveforms and fetal biometry at 20 + 0 to 23 + 6 and 34 + 0 to 37 + 6 week's gestation. Several predictive models were constructed, using supervised learning techniques, and evaluated using the Stochastic Gradient Descent, k-Nearest Neighbours, Logistic Regression and Random Forest methods.

The final model performed exceptionally well across all evaluation metrics, particularly so for the Stochastic Gradient Descent method achieving a 93% average for Classification Accuracy, Recall, Precision and F1-Score when random sampling is used and 91% for cross-validation (both methods using a 95% confidence interval).