Reduction in patient burdens with graphical computerized adaptive testing on the ADL scale: tool development and simulation

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to verify the effectiveness and efficacy of saving time and reducing burden for patients, nurses, and even occupational therapists through computer adaptive testing (CAT).</p> <p>Methods</p> <p>Based on an item bank of the Barthel Index (BI) and the Frenchay Activities Index (FAI) for assessing comprehensive activities of daily living (ADL) function in stroke patients, we developed a visual basic application (VBA)-Excel CAT module, and (1) investigated whether the averaged test length via CAT is shorter than that of the traditional all-item-answered non-adaptive testing (NAT) approach through simulation, (2) illustrated the CAT multimedia on a tablet PC showing data collection and response errors of ADL clinical functional measures in stroke patients, and (3) demonstrated the quality control of endorsing scale with fit statistics to detect responding errors, which will be further immediately reconfirmed by technicians once patient ends the CAT assessment.</p> <p>Results</p> <p>The results show that endorsed items could be shorter on CAT (<it>M </it>= 13.42) than on NAT (<it>M </it>= 23) at 41.64% efficiency in test length. However, averaged ability estimations reveal insignificant differences between CAT and NAT.</p> <p>Conclusion</p> <p>This study found that mobile nursing services, placed at the bedsides of patients could, through the programmed VBA-Excel CAT module, reduce the burden to patients and save time, more so than the traditional NAT paper-and-pencil testing appraisals.</p

Nanoscale Nucleation and Growth of Non-Stoichiometric V-Shaped InP Defect in Heterogeneous InGaAsP/InP Array

Nanotechnology is a broad field that involves the manipulation of atoms and molecules. For nanophotonics, defect formation in nanostructured compound semiconductor system is of great technological interest. In this paper, we study the nanoscale nucleation and growth of V-shaped defect in the heterogeneous InGaAsP/InP array. We have observed that the nucleation originated from the phosphorus-deficient disordering that was likely induced by reactive ion etching. During the nucleation, the phosphorus-deficient In1+xP1-x compound was developed at the trench. The triangular nano-precipitates of In1+xP1-x with sizes of 20-30nm were formed. The ratio of In to P in the non-stoichiometric compound was higher in the upper portion of the V-defect, likely due to antisite defect mechanism. During the defect growth process, the phosphorus-deficient nucleation site expanded to form open, inverted pyramid with sidewalls following the crystallographic planes

Biogeochemical cycling of carbon, nitrogen and phosphorus in the North Sea (CANOPY)

info:eu-repo/semantics/publishe

Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study

The prediction of whether active NBA players can be inducted into the Hall of Fame (HOF) is interesting and important. However, no such research have been published in the literature, particularly using the artificial neural network (ANN) technique. The aim of this study is to build an ANN model with an app for automatic prediction and classification of HOF for NBA players. We downloaded 4728 NBA players’ data of career stats and accolades from the website at basketball-reference.com. The training sample was collected from 85 HOF members and 113 retired Non-HOF players based on completed data and a longer career length (≥15 years). Featured variables were taken from the higher correlation coefficients (&lt;0.1) with HOF and significant deviations apart from the two HOF/Non-HOF groups using logistical regression. Two models (i.e., ANN and convolutional neural network, CNN) were compared in model accuracy (e.g., sensitivity, specificity, area under the receiver operating characteristic curve, AUC). An app predicting HOF was then developed involving the model’s parameters. We observed that (1) 20 feature variables in the ANN model yielded a higher AUC of 0.93 (95% CI 0.93–0.97) based on the 198-case training sample, (2) the ANN performed better than CNN on the accuracy of AUC (= 0.91, 95% CI 0.87–0.95), and (3) an ready and available app for predicting HOF was successfully developed. The 20-variable ANN model with the 53 parameters estimated by the ANN for improving the accuracy of HOF has been developed. The app can help NBA fans to predict their players likely to be inducted into the HOF and is not just limited to the active NBA players

The Implementation of Sepsis Bundles on the Outcome of Patients with Severe Sepsis or Septic Shock in Intensive Care Units

SummaryBackgroundThe goal of the study was to implement sepsis bundles and examine the effect on patients with severe sepsis or septic shock in intensive care units (ICUs).MethodsAll patients with severe sepsis or septic shock admitted to the 13-bed ICU were included. Sepsis bundles were implemented within 24 hours after admission. The implementation of sepsis bundles was categorized into preintervention (January to April 2010), education (July to October 2010), operational (November to December 2010), and postintervention (January to April 2011) phases. Comparison of bundle compliance and outcome between each phase were examined. We also found mortality predictors between preintervention and postintervention phases.ResultsThere were 164 patients included in the study. Compared with the preintervention phase, the bundle compliance of each phase (education, operation, and postintervention separately) was higher (43.3%, 84.6%, and 79.2%, respectively, vs. 20.0%, p < 0.05), the hospital mortality was lower (10.0%, 23.1%, and 24.5%, respectively, vs. 43.6%, p < 0.05). Under multivariate analyses, the predictors for mortality between the preintervention and postintervention phases were: lactate at ICU (odds ratio [OR] 2.212), urinary tract infection (OR 0.026), and postintervention (OR 0.239).ConclusionImplementation of modified sepsis bundles was successful in changing sepsis treatment behavior and was associated with a substantial reduction in hospital mortality and trends of decreased hospital expenditure. Factors improved hospital mortality, as lower lactate levels at ICU, urinary tract infection, and postintervention. The proposed intervention is generally applicable to achieve similar improvements

Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development

The COVID-19 pandemic has spread widely around the world. Many mathematical models have been proposed to investigate the inflection point (IP) and the spread pattern of COVID-19. However, no researchers have applied social network analysis (SNA) to cluster their characteristics. We aimed to illustrate the use of SNA to identify the spread clusters of COVID-19. Cumulative numbers of infected cases (CNICs) in countries/regions were downloaded from GitHub. The CNIC patterns were extracted from SNA based on CNICs between countries/regions. The item response model (IRT) was applied to create a general predictive model for each country/region. The IP days were obtained from the IRT model. The location parameters in continents, China, and the United States were compared. The results showed that (1) three clusters (255, n = 51, 130, and 74 in patterns from Eastern Asia and Europe to America) were separated using SNA, (2) China had a shorter mean IP and smaller mean location parameter than other counterparts, and (3) an online dashboard was used to display the clusters along with IP days for each country/region. Spatiotemporal spread patterns can be clustered using SNA and correlation coefficients (CCs). A dashboard with spread clusters and IP days is recommended to epidemiologists and researchers and is not limited to the COVID-19 pandemic