Background Characteristics

The use of reference management software in the context of academic work and research is the main subject of this study. The study focuses on the extent to which postgraduate students at the Aristotle University of Thessaloniki, one of the largest Greek universities, make use of – or avoid using – reference management software tools to organize their bibliographic databases and to automate the process of creating references and citations. The study also tries to find out which are the key factors for their choices and whether certain background characteristics affect their behavior. It should be mentioned that no previous studies have been conducted in Greece regarding the use of reference management software in the academic environment. An online questionnaire was sent to the postgraduate students at the University and a result set of 545 responses was collected and analyzed. The majority (almost two-thirds) of the respondents identified themselves as non-users and one-third identified themselves as reference management software users. Among the latter, Mendeley was found to be the software used by more than two-thirds of the users and was followed by EndNote and Zotero. It is worth mentioning that Mendeley is the software officially recommended by the University’s central library to its users but most of the students (more than 60%) were not aware of this fact. In terms of background characteristics, the analysis revealed, among other things, statistically significant relationships between degree level, student discipline and preferences, reference management software features, and potential future use of reference management software.

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Catheter ablation can improve survival with the reduction of heart failure in frail patients with atrial fibrillation

European Heart Journal ◽

10.1093/ehjci/ehaa946.0557 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

B Joung ◽

P.S Yang ◽

J.H Sung ◽

E Jang ◽

H.T Yu ◽

...

Keyword(s):

Myocardial Infarction ◽

Heart Failure ◽

Atrial Fibrillation ◽

Acute Myocardial Infarction ◽

Catheter Ablation ◽

Real World ◽

Background Characteristics ◽

Risk Of Death ◽

Frail Patients ◽

Korean National Health Insurance

Abstract Background It is unclear whether catheter ablation is beneficial in frail patients with AF. Purpose This study aimed to evaluate whether catheter ablation reduces death and other outcomes in real-world frail patients with atrial fibrillation (AF). Methods Out of 801,710 patients with AF in the Korean National Health Insurance Service database from 2006 to 2015, 1,411 frail patients underwent AF ablations. The Hospital Frailty Risk Score were calculated retrospectively. Inverse probability of treatment weighting (IPTW) was used to categorize ablation and non-ablation frail groups. Results After IPTW, the two cohorts had similar background characteristics. During a median follow-up of 4.7 years (interquartile range: 2.2–7.8), the risk of death in frail patients with ablations was reduced by 65% compared to frail patients without ablations (2.0 and 6.4 per 100 person-years, respectively; hazard ratio [HR] 0.35; 95% confidence interval [CI] 0.25–0.50; P<0.001). Ablations were related with a lower incidence and risk of heart failure admission (1.8 and 3.1 per 100 person-years, respectively; HR 0.66, 95% CI 0.44–0.98; P=0.042) and acute myocardial infarction (0.2 and 0.6 per 100 person-years, respectively; HR 0.30, 95% CI 0.15–0.62; P=0.001). However, the risk of stroke did not change after ablation. Conclussion Ablation may be associated with lower incidences of death, heart failure, and acute myocardial infarction in real-world frail patients with AF, supporting the role of AF ablation in these patients. The effect of frailty risk on the outcome of ablation should be evaluated in further studies. Funding Acknowledgement Type of funding source: None

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‘What about me?’ Stories of the educational experiences of care-experienced children and young people in a Scottish local authority

Adoption & Fostering ◽

10.1177/03085759211011736 ◽

2021 ◽

Vol 45 (2) ◽

pp. 173-190

Author(s):

Daniela Mercieca ◽

Duncan P Mercieca ◽

Leisa Randall

Keyword(s):

Academic Achievement ◽

Young People ◽

Local Authority ◽

School Attendance ◽

Educational Experiences ◽

Influential Factors ◽

Background Characteristics ◽

Children And Young People ◽

Looked After Children ◽

Home And School

This qualitative study explores the educational experiences of looked after children and young people in one Scottish local authority. The preoccupations of government are academic achievement and school attendance, but these are not the prime concerns of the children, carers and professionals involved. Moreover, they can be both enhanced and restricted by the background characteristics and care situations of the young people and the responses of schools to their needs and behaviour. Five influential factors emerged from interviews and focus groups with professionals, carers and young people: behaviour; school attendance; carers as educators; friendships; and communication between home and school. Each of them is discussed with extended quotations that convey the voices of participants.

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Bayesian adaptive design of early-phase clinical trials for precision medicine based on cancer biomarkers

The International Journal of Biostatistics ◽

10.1515/ijb-2021-0009 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Shinjo Yada

Keyword(s):

Neural Network ◽

Clinical Trials ◽

Precision Medicine ◽

Early Phase ◽

Patient Specific ◽

Specific Gene ◽

Cancer Type ◽

Background Characteristics ◽

Number Of Patients ◽

Early Phase Clinical Trials

Abstract Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.

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