Cognitive Remediation for Individuals with Psychosis in a Supported Education Setting: A Pilot Study

Cognitive remediation (CR) is a treatment approach that is being increasingly examined as a means through which the cognitive impacts of schizophrenia might be ameliorated. While CR has demonstrated good outcomes when paired with supported employment, little is known regarding how it might be integrated within supported education contexts. In this study CR was examined in a supported education context with 16 individuals with psychosis. The findings indicated that CR aligned well with the academic curriculum with very low attrition, was found useful by students, and showed similar pre-post differences on cognitive measures as those found in previous work

Assessment of strategies for switching patients from olanzapine to risperidone: A randomized, open-label, rater-blinded study

<p>Abstract</p> <p>Background</p> <p>In clinical practice, physicians often need to change the antipsychotic medications they give to patients because of an inadequate response or the presence of unacceptable or unsafe side effects. However, there is a lack of consensus in the field as to the optimal switching strategy for antipsychotics, especially with regards to the speed at which the dose of the previous antipsychotic should be reduced. This paper assesses the short-term results of strategies for the discontinuation of olanzapine when initiating risperidone.</p> <p>Methods</p> <p>In a 6-week, randomized, open-label, rater-blinded study, patients with schizophrenia or schizoaffective disorder, on a stable drug dose for more than 30 days at entry, who were intolerant of or exhibiting a suboptimal symptom response to more than 30 days of olanzapine treatment, were randomly assigned to the following switch strategies (common risperidone initiation scheme; varying olanzapine discontinuation): (i) abrupt strategy, where olanzapine was discontinued at risperidone initiation; (ii) gradual 1 strategy, where olanzapine was given at 50% entry dose for 1 week after risperidone initiation and then discontinued; or (iii) gradual 2 strategy, where olanzapine was given at 100% entry dose for 1 week, then at 50% in the second week, and then discontinued.</p> <p>Results</p> <p>The study enrolled 123 patients on stable doses of olanzapine. Their mean age was 40.3 years and mean (± standard deviation (SD)) baseline Positive and Negative Syndrome Scale (PANSS) total score of 75.6 ± 11.5. All-cause treatment discontinuation was lowest (12%) in the group with the slowest olanzapine dose reduction (gradual 2) and occurred at half the discontinuation rate in the other two groups (25% in abrupt and 28% in gradual 1). The relative risk of early discontinuation was 0.77 (confidence interval 0.61–0.99) for the slowest dose reduction compared with the other two strategies. After the medication was changed, improvements at endpoint were seen in PANSS total score (-7.3; <it>p </it>< 0.0001) and in PANSS positive (-3.0; <it>p </it>< 0.0001), negative (-0.9; <it>p </it>= 0.171) and anxiety/depression (-1.4; <it>p </it>= 0.0005) subscale scores. Severity of movement disorders and weight changes were minimal.</p> <p>Conclusion</p> <p>When switching patients from olanzapine to risperidone, a gradual reduction in the dose of olanzapine over 2 weeks was associated with higher rates of retention compared with abrupt or less gradual discontinuation. Switching via any strategy was associated with significant improvements in positive and anxiety symptoms and was generally well tolerated.</p> <p>Trial registration</p> <p>ClinicalTrials.gov NCT00378183</p

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG- bench). BIG-bench currently consists of 204 tasks, contributed by 450 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood develop- ment, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google- internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting