Knowledge Utilization, Coordination, and Team Performance

Considerable research has established the superior performance of teams on which team members utilize specialized knowledge and also develop transactive processes that promote coordination. Less is known, however, about the consequences for team performance when team members only possess one of the two productivity factors. We develop and test a framework highlighting the distinct challenges these teams will face. In particular, our results show that each productivity factor contributed significantly more to team performance when the other factor was present. And our findings also illustrate a potential failure mode for knowledge utilization. If team members could not coordinate their collective efforts, utilizing knowledge undermined team performance. Our framework outlines a similar risk for too much coordination, if team members cannot utilize their specialized knowledge and are asked to perform a task with a “rugged” performance landscape. We discuss the implications of our framework and results for theory and practice

Initial experience of dedicated breast PET imaging of ER+ breast cancers using [F-18]fluoroestradiol.

Dedicated breast positron emission tomography (dbPET) is an emerging technology with high sensitivity and spatial resolution that enables detection of sub-centimeter lesions and depiction of intratumoral heterogeneity. In this study, we report our initial experience with dbPET using [F-18]fluoroestradiol (FES) in assessing ER+ primary breast cancers. Six patients with >90% ER+ and HER2- breast cancers were imaged with dbPET and breast MRI. Two patients had ILC, three had IDC, and one had an unknown primary tumor. One ILC patient was treated with letrozole, and another patient with IDC was treated with neoadjuvant chemotherapy without endocrine treatment. In this small cohort, we observed FES uptake in ER+ primary breast tumors with specificity to ER demonstrated in a case with tamoxifen blockade. FES uptake in ILC had a diffused pattern compared to the distinct circumscribed pattern in IDC. In evaluating treatment response, the reduction of SUVmax was observed with residual disease in an ILC patient treated with letrozole, and an IDC patient treated with chemotherapy. Future study is critical to understand the change in FES SUVmax after endocrine therapy and to consider other tracer uptake metrics with SUVmax to describe ER-rich breast cancer. Limitations include variations of FES uptake in different ER+ breast cancer diseases and exclusion of posterior tissues and axillary regions. However, FES-dbPET has a high potential for clinical utility, especially in measuring response to neoadjuvant endocrine treatment. Further development to improve the field of view and studies with a larger cohort of ER+ breast cancer patients are warranted

Decision-making in crisis resolution and home treatment teams: The AWARE framework

The aim of the study is to improve patient safety by identifying factors influencing gatekeeping decisions by crisis resolution and home treatment teams. A theoretical sampling method was used to recruit clinicians. Semi-structured interviews to elicit various aspects of clinical decision-making were carried out. The transcripts were thematically analysed using a grounded theory approach.ResultsPatient needs (safety and treatment) was the primary driver behind decisions. The research also revealed that information gathered was processed using heuristics. We identified five key themes (anxiety, weighting, agenda, resource and experience), which were constructed into an acronym ‘AWARE’.Clinical implicationsAWARE provides a framework to make explicit drivers for decision-making that are often implicit. Incorporating these drivers into reflective practice will help staff be more mindful of undue influences and result in improved clinical decisions.Declaration of interestNone.</jats:sec

Lignin recovery from cocoa bean shell using microwave-assisted extraction and deep eutectic solvents

Lignin is the second most abundant natural polymer after cellulose, and valorisation of lignin-rich streams has attracted increasing attention recently. This paper presents a novel and sustainable method to recover lignin from Cocoa Bean Shells (CBS) using Deep Eutectic Solvents (DES) and microwaves. A DES containing p-toluenesulfonic acid, choline chloride and glycerol (2:1:1 M ratio) was selected based on its dielectric properties. Under 200 W microwave power, the optimum yield of 95.5 % lignin was achieved at 130 °C and 30 min. DES-extracted lignin exhibited unique structural characteristics including larger particle sizes (242.5 µm D50 size), structural diversity (410.4 µm D90-D10 size) and H/G sub-unit ratio (71.9 %) compared with commercial Kraft lignin (77.2 µm, 157.9 µm and 0.1 % respectively), indicating the potential of DES in the modification and upgrading of lignin for novel value-added products

Serum HCoV-spike specific antibodies do not protect against subsequent SARS-CoV-2 infection in children and adolescents

SARS-CoV-2 infections in children are generally asymptomatic or mild and rarely progress to severe disease and hospitalization. Why this is so remains unclear. Here we explore the potential for protection due to pre-existing cross-reactive seasonal coronavirus antibodies and compare the rate of antibody decline for nucleocapsid and spike protein in serum and oral fluid against SARS-CoV-2 within the pediatric population. No differences in seasonal coronaviruses antibody concentrations were found at baseline between cases and controls, suggesting no protective effect from pre-existing immunity against seasonal coronaviruses. Antibodies against seasonal betacoronaviruses were boosted in response to SARS-CoV-2 infection. In serum, anti-nucleocapsid antibodies fell below the threshold of positivity more quickly than anti-spike protein antibodies. These findings add to our understanding of protection against infection with SARS-CoV-2 within the pediatric population, which is important when considering pediatric SARS-CoV-2 immunization policies

Proceedings of Patient Reported Outcome Measure’s (PROMs) Conference Oxford 2017: Advances in Patient Reported Outcomes Research

A33-Effects of Out-of-Pocket (OOP) Payments and Financial Distress on Quality of Life (QoL) of People with Parkinson’s (PwP) and their Carer

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Characteristics of Adults in the Hepatitis B Research Network in North America Reflect Their Country of Origin and Hepatitis B Virus Genotype

Chronic hepatitis B virus (HBV) infection is an important cause of cirrhosis and hepatocellular carcinoma worldwide; populations that migrate to the US and Canada might be disproportionately affected. The Hepatitis B Research Network (HBRN) is a cooperative network of investigators from the United States and Canada, created to facilitate clinical, therapeutic, and translational research in adults and children with hepatitis B. We describe the structure of the network and baseline characteristics of adults with hepatitis B enrolled in the network