A reliable method of predicting large vessel occlusion (LVO) stroke in data sets without neuroimaging could be retrospectively applied to expand research efforts. We conducted a retrospective, cross-sectional cohort analysis of the Get With The Guidelines (GWTG)-Stroke registry. We included adult patients with a final diagnosis of ischemic stroke from 2016 to 2021 who had brain and vascular imaging and excluded those with missing data or posterior circulation stroke. We included 416,022 patients of which 125,381 (30.1%) had LVO. The mean age was 71 years, and 48.2% were female. The area under the receiver operating curve (AUC) for the final model, including age, sex, hypertension, dyslipidemia, atrial fibrillation, diabetes, TOAST stroke mechanism, and NIH Stroke Scale (NIHSS), was 0.79 (95% CI 0.79-0.80). Without TOAST mechanism, the AUC was 0.74. The specificity did not exceed 0.5 using different cut points for the NIHSS. We found that 30% of adult acute ischemic stroke patients in GWTG-Stroke have LVO and that the combination of clinical covariates and NIHSS is only moderately predictive of LVO status. These results are consistent with previous studies and suggest it may not be possible to retrospectively predict LVO with high accuracy in data sets without vascular neuroimaging.

Large language models (LLMs) are advanced artificial intelligence (AI) systems that excel in recognizing and generating human-like language, possibly serving as valuable tools for neurology-related information tasks. Although LLMs have shown remarkable potential in various areas, their performance in the dynamic environment of daily clinical practice remains uncertain. This article outlines multiple limitations and challenges of using LLMs in clinical settings that need to be addressed, including limited clinical reasoning, variable reliability and accuracy, reproducibility bias, self-serving bias, sponsorship bias, and potential for exacerbating health care disparities. These challenges are further compounded by practical business considerations and infrastructure requirements, including associated costs. To overcome these hurdles and harness the potential of LLMs effectively, this article includes considerations for health care organizations, researchers, and neurologists contemplating the use of LLMs in clinical practice. It is essential for health care organizations to cultivate a culture that welcomes AI solutions and aligns them seamlessly with health care operations. Clear objectives and business plans should guide the selection of AI solutions, ensuring they meet organizational needs and budget considerations. Engaging both clinical and nonclinical stakeholders can help secure necessary resources, foster trust, and ensure the long-term sustainability of AI implementations. Testing, validation, training, and ongoing monitoring are pivotal for successful integration. For neurologists, safeguarding patient data privacy is paramount. Seeking guidance from institutional information technology resources for informed, compliant decisions, and remaining vigilant against biases in LLM outputs are essential practices in responsible and unbiased utilization of AI tools. In research, obtaining institutional review board approval is crucial when dealing with patient data, even if deidentified, to ensure ethical use. Compliance with established guidelines like SPIRIT-AI, MI-CLAIM, and CONSORT-AI is necessary to maintain consistency and mitigate biases in AI research. In summary, the integration of LLMs into clinical neurology offers immense promise while presenting formidable challenges. Awareness of these considerations is vital for harnessing the potential of AI in neurologic care effectively and enhancing patient care quality and safety. The article serves as a guide for health care organizations, researchers, and neurologists navigating this transformative landscape.

Poststroke epilepsy (PSE) is associated with higher mortality and poor functional and cognitive outcomes in patients with stroke. With the remarkable development of acute stroke treatment, there is a growing number of survivors with PSE. Although approximately 10% of patients with stroke develop PSE, given the significant burden of stroke worldwide, PSE is a significant problem in stroke survivors. Therefore, the attention of health policymakers and significant funding are required to promote PSE prevention research. The current PSE definition includes unprovoked seizures occurring more than 7 days after stroke onset, given the high recurrence risks of seizures. However, the pathologic cascade of stroke is not uniform, indicating the need for a tissue-based approach rather than a time-based one to distinguish early seizures from late seizures. EEG is a commonly used tool in the diagnostic work-up of PSE. EEG findings during the acute phase of stroke can potentially stratify the risk of subsequent seizures and predict the development of poststroke epileptogenesis. Recent reports suggest that cortical superficial siderosis, which may be involved in epileptogenesis, is a promising marker for PSE. By incorporating such markers, future risk-scoring models could guide treatment strategies, particularly for the primary prophylaxis of PSE. To date, drugs that prevent poststroke epileptogenesis are lacking. The primary challenge involves the substantial cost burden due to the difficulty of reliably enrolling patients who develop PSE. There is, therefore, a critical need to determine reliable biomarkers for PSE. The goal is to be able to use them for trial enrichment and as a surrogate outcome measure for epileptogenesis. Moreover, seizure prophylaxis is essential to prevent functional and cognitive decline in stroke survivors. Further elucidation of factors that contribute to poststroke epileptogenesis is eagerly awaited. Meanwhile, the regimen of antiseizure medications should be based on individual cardiovascular risk, psychosomatic comorbidities, and concomitant medications. This review summarizes the current understanding of poststroke epileptogenesis, its risks, prognostic models, prophylaxis, and strategies for secondary prevention of seizures and suggests strategies to advance research on PSE.

Knowledge of young-onset Alzheimer disease in adults with Down syndrome has greatly improved clinical care. However, little is known about dementia in rare genetic neurodevelopmental disorders (RGNDs). In this review, a comprehensive overview is provided of reports on dementia and cognitive/adaptive trajectories in adults with RGNDs. A systematic literature review was conducted in Embase, Medline ALL, and PsycINFO on December 6, 2022. The protocol was registered in PROSPERO (CRD42021223041). Search terms for dementia, cognitive and adaptive functioning, and RGNDs were combined using generic terms and the Orphanet database. Study characteristics and descriptive data on genetic diagnosis, clinical and neuropathologic features, comorbidities, and diagnostic methods were extracted using a modified version of the Cochrane Data Extraction Template. The literature search yielded 40 publications (17 cohorts, 23 case studies) describing dementia and/or cognitive or adaptive trajectories in adults with 14 different RGNDs. Dementia was reported in 49 individuals (5 cohorts, 20 cases) with a mean age at onset of 44.4 years. Diagnostics were not disclosed for half of the reported individuals (n = 25/49, 51.0%). A total of 44 different psychodiagnostic instruments were used. MRI was the most reported additional investigation (n = 12/49, 24.5%). Comorbid disorders most frequently associated with cognitive/adaptive decline were epilepsy, psychotic disorders, and movement disorders. Currently available literature shows limited information on aging in RGNDs, with relatively many reports of young-onset dementia. Longitudinal data may provide insights into converging neurodevelopmental degenerative pathways. We provide recommendations to optimize dementia screening, diagnosis, and research.

Parkinson disease (PD) remains a progressive and incurable disease. Research over the past decade provides strong evidence of a detectible phase before the clinical diagnosis, known as the prodromal phase of PD (pPD). In this article, we review the debate about disclosure of risk of progression to PD and related disorders to individuals through the perspectives of the pillars of medical ethics: beneficence, nonmaleficence, autonomy, and justice. There is evidence that lifestyle modification may have positive effects on onset and progression of PD, providing justification of potential benefit. From a societal perspective, a diagnosis of pPD could allow targeted recruitment to disease-modifying trials. Regarding nonmaleficence, direct evidence that catastrophic reactions are scarce is largely derived from studies of monogenic conditions, which may not be generalizable. Diagnosis of PD can be traumatic, and appropriate communication and evaluation of circumstances to weigh up disclosure is crucial. Future research should therefore examine the potential harms of early and of false-positive diagnoses and specifically examine these matters in diverse populations. Autonomy balances the right to know and the right not to know, so an individualized patient-centered approach and shared decision-making is essential, acknowledging that knowledge of being in the prodromal phase could prolong autonomy in the longer term. Distributive justice brings focus toward health care and related planning at the individual and societal level and affects the search for disease modification in PD. We must acknowledge that waiting for established disease states is likely to be too little, too late and results in failures of expensive trials and wasted participant and researcher effort. Ultimately, clinicians must arrive at a decision with the patient that solicits and integrates patients' goals, taking into account their individual life circumstances, perspectives, and philosophies, recognizing that one size cannot fit all.

Degeneration of the presynaptic nigrostriatal dopaminergic system is one of the main biological features of Parkinson disease (PD), multiple system atrophy (MSA), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD), which can be measured using single-photon emission CT imaging for diagnostic purposes. Despite its widespread use in clinical practice and research, the diagnostic properties of presynaptic nigrostriatal dopaminergic (DAT) imaging in parkinsonism have never been evaluated against the diagnostic gold standard of neuropathology. The aim of this study was to evaluate the diagnostic parameters of DAT imaging compared with pathologic diagnosis in patients with parkinsonism. Retrospective cohort study of patients with DAT imaging for the investigation of a clinically uncertain parkinsonism with brain donation between 2010 and 2021 to the Queen Square Brain Bank (London). Patients with DAT imaging for investigation of pure ataxia or dementia syndromes without parkinsonism were excluded. Those with a pathologic diagnosis of PD, MSA, PSP, or CBD were considered presynaptic dopaminergic parkinsonism, and other pathologies were considered postsynaptic for the analysis. DAT imaging was performed in routine clinical practice and visually classified by hospital nuclear medicine specialists as normal or abnormal. The results were correlated with neuropathologic diagnosis to calculate diagnostic accuracy parameters for the diagnosis of presynaptic dopaminergic parkinsonism. All of 47 patients with PD, 41 of 42 with MSA, 68 of 73 with PSP, and 6 of 10 with CBD (sensitivity 100%, 97.6%, 93.2%, and 60%, respectively) had abnormal presynaptic dopaminergic imaging. Eight of 17 patients with presumed postsynaptic parkinsonism had abnormal scans (specificity 52.9%). DAT imaging has very high sensitivity and negative predictive value for the diagnosis of presynaptic dopaminergic parkinsonism, particularly for PD. However, patients with CBD, and to a lesser extent PSP (of various phenotypes) and MSA (with predominant ataxia), can show normal DAT imaging. A range of other neurodegenerative disorders may have abnormal DAT scans with low specificity in the differential diagnosis of parkinsonism. DAT imaging is a useful diagnostic tool in the differential diagnosis of parkinsonism, although clinicians should be aware of its diagnostic properties and limitations. This study provides Class II evidence that DAT imaging does not accurately distinguish between presynaptic dopaminergic parkinsonism and non-presynaptic dopaminergic parkinsonism.

The motivation for conducting this study is to investigate the potential of Cloud e-learning to address the high-cost and high-complexity challenges of conventional learning methods for the upgraded learning processes in higher education. The overall direction of this research is driven towards how the actual usage of Cloud e-learning module affects students’ perceptions and academic performance. A Cloud e-learning module is designed and developed to promote optimised resource utilisation in the e-learning processes in higher education. A pretest-posttest method was adopted to study the impact of Cloud e-learning usage among students and whether the diffusion of Cloud e-learning has caused a change in students’ perceptions. The pretest-posttest results and students’ academic performance were then analysed to examine the impact from the actual usage of Cloud e-learning module. The findings reveal that the change of students’ perceptions is time variant, indicating students’ mixed perceptions on the usage of Cloud e-learning module. Analysis evidently reveals that the use of Cloud e-learning improved students’ learning performance in theoretical subjects. This research is useful to educators and ICT practitioners in making informed decisions in adopting the right ICT infrastructures to support e-learning in higher education.

ABSTRACT Race structures the lives of Indigenous peoples and other negatively racialized people in Australia. The language of race permeates institutions, workplaces, and is embedded in everyday life. Racial literacy as pedagogical praxis resists the racial contract that secures whiteness as a structure of possessive power. We explore the state of racial literacy in Australian education, and the obstructions and opportunities for educators to do anti-racism work. To this end, we draw on the preliminary findings from an ongoing research project, Breaking the Racial Silence, and use them to inform a theoretical framework for conceptualizing educators’ racial literacy practices. We bring to the fore effective strategies, practices, and programmes used to interrogate race and racism. Despite the efforts of educators, racial literacy in formal and informal education is oftentimes suppressed and concealed by languages and practices that sustain the practices and pedagogies of whiteness.

We examined how Learning Analytics literature represents participants from diverse societies by comparing the studies published with samples from WEIRD (Western, Industrialized, Rich, Democratic) nations versus non-WEIRD nations. By analyzing the Learning Analytics studies published during 2015-2019 (N = 360), we found that most of the studies were on WEIRD samples, with at least 58 percent of the total studies on WEIRD samples. Through keyword analysis, we found that the studies on WEIRD samples’ research topics focused on self-regulated learning and feedback received in learning environments. The studies on non-WEIRD samples focused on the collaborative and social nature of learning. Our investigation of the analysis tools used for the studies suggested the limitations of some software in analyzing languages in diverse countries. Our analysis of theoretical frameworks revealed that most studies on both WEIRD and non-WEIRD samples did not identify a theoretical framework. The studies on WEIRD and non-WEIRD samples convey the similarities of Learning Analytics and Educational Data Mining. We conclude by discussing the importance of integrating multifaceted elements of the participant samples, including cultural values, societal values, and geographic areas, and present recommendations on ways to promote inclusion and diversity in Learning Analytics research.

In a rapidly emerging world, knowledge management capabilities, including knowledge creation, acquisition, sharing, and utilization, become more critical for organizational change, growth, and competitiveness. Therefore, this article argues that organizations should consider implementing effective journal club meetings as an opportunity to acquire external knowledge, evaluate and integrate new knowledge into previous existing knowledge, and disseminate and utilize new knowledge to enhance product or service quality, innovation, and the performance of an organization. The article reviews relevant literature, proposes a framework for conducting effective journal club meetings, and aligns those activities with the knowledge management processes. The article proposes a framework for conducting effective journal club meetings and a process that integrates both journal club activities and the knowledge management process. By adopting this framework, journal club activities would be more effective in developing new knowledge management capabilities among individual members and enhancing organizational performance, i.e., implementing evidence-based practices (EBP), improving service quality, and producing research.