Dishonest Academic Conduct: From the Perspective of the Utility Function

Comparison of Antipsychotics for the Treatment of Patients With Delirium and QTc Interval Prolongation: A Clinical Decision Analysis

Background: Antipsychotics are frequently used to treat delirium but often induce corrected QT (QTc) prolongation, which can be lethal by causing torsade de pointes. Nonetheless, the selection of antipsychotics to treat delirium patients with prolonged baseline QTc intervals remains unclear. We aimed to assess the utility of antipsychotics based on their effects on treatment outcomes and QTc intervals. Methods: A clinical decision analysis was conducted using data on the effects of antipsychotics on treatment outcomes and QTc intervals from published network meta-analyses. We quantified the utility of six antipsychotics (amisulpride, haloperidol, olanzapine, quetiapine, risperidone, and ziprasidone) using a decision tree and the obtained effect sizes. Subsequently, we conducted sensitivity analyses using multiple utility settings and another dataset. We also performed a probabilistic sensitivity analysis using Monte Carlo simulation, in which the effects of antipsychotics were randomly sampled given the plausible range. Results: Amisulpride showed the highest utility when the baseline QTc interval was 420 ms. Quetiapine showed the highest utility when the baseline QTc interval was ≥450 ms. The sensitivity analyses also showed the superiority of quetiapine when the baseline QTc intervals were prolonged. Conclusions: Decision analysis suggests that quetiapine is the optimal antipsychotic drug for the treatment of patients with delirium and prolonged baseline QTc intervals.

Impact of pressure, self-efficacy, and self-competency on students' plagiarism in higher education

To explore students' plagiarism in higher level education, we designed a quantitative study and collected data from enrolled university students from Islamabad, the capital city of Pakistan. This was done by distributing a web-link for an online survey (Google form) through WhatsApp social media mobile software. We applied structural equation modeling (SEM) techniques by using IBM SPSS AMOS 24.0.0 software to analyze collected data. The research findings suggest that some human factors do in fact exist and that these factors drive students to engage in certain unethical practices of plagiarism. Apart from poor training and lack of skills on the students' part, the pressures and the self-efficacy they face as they engage in research practices can make students susceptible to plagiarize.