XR in Aviation Training: Insight from Academia, Industry, and Non-Profit Institutions

The COVID-19 pandemic had a profound impact on education and training. Institutions that relied heavily on face-to-face instruction suddenly needed alternative modalities to keep students on course, forcing educators and trainers to employ a variety of educational techniques via technologies that they may not have had experience with. This shift has brought the advantages – and disadvantages – of augmented, mixed, and virtual reality technologies (collectively, extended reality or XR) for education and training into sharp focus. Programs were quickly assembled, and not always with consideration of learning theories. As learning and training were resumed in in-person settings, academics and industry alike were faced with a new challenge: How do we continue to develop XR technologies to leverage efficiencies and expand opportunities without sacrificing learning and training outcomes? This question has brought researchers, practitioners, developers, and innovators together into an XR Research Consortium to advocate for the design, evaluation, implementation, and sharing of findings of XR technology in a variety of learning and training environments. Although many of the members have a background in aviation and aerospace, a goal of the Consortium is to expand into other industries and promote XR technologies as educational tools. Members of the Consortium will discuss: Current research, gaps in the research, potential for XR Using XR to make learning/training more accessible Choosing an XR technology that aligns with learning/training outcomes Cybersecurity considerations of XR in a learning/training environment Demonstrations of XR applications for training will be included. The session will include time for an open discussion

Prevalence of all epilepsies in urban informal settlements in Nairobi, Kenya: a two-stage population-based study

BACKGROUND: WHO estimates that more than 50 million people worldwide have epilepsy and 80% of cases are in low-income and middle-income countries. Most studies in Africa have focused on active convulsive epilepsy in rural areas, but there are few data in urban settings. We aimed to estimate the prevalence and spatial distribution of all epilepsies in two urban informal settlements in Nairobi, Kenya. METHODS: We did a two-stage population-based cross-sectional study of residents in a demographic surveillance system covering two informal settlements in Nairobi, Kenya (Korogocho and Viwandani). Stage 1 screened all household members using a validated epilepsy screening questionnaire to detect possible cases. In stage 2, those identified with possible seizures and a proportion of those screening negative were invited to local clinics for clinical and neurological assessments by a neurologist. Seizures were classified following the International League Against Epilepsy recommendations. We adjusted for attrition between the two stages using multiple imputations and for sensitivity by dividing estimates by the sensitivity value of the screening tool. Complementary log-log regression was used to assess prevalence differences by participant socio-demographics. FINDINGS: A total of 56 425 individuals were screened during stage 1 (between Sept 17 and Dec 23, 2021) during which 1126 were classified as potential epilepsy cases. A total of 873 were assessed by a neurologist in stage 2 (between April 12 and Aug 6, 2022) during which 528 were confirmed as epilepsy cases. 253 potential cases were not assessed by a neurologist due to attrition. 30 179 (53·5%) of the 56 425 individuals were male and 26 246 (46·5%) were female. The median age was 24 years (IQR 11-35). Attrition-adjusted and sensitivity-adjusted prevalence for all types of epilepsy was 11·9 cases per 1000 people (95% CI 11·0-12·8), convulsive epilepsy was 8·7 cases per 1000 people (8·0-9·6), and non-convulsive epilepsy was 3·2 cases per 1000 people (2·7-3·7). Overall prevalence was highest among separated or divorced individuals at 20·3 cases per 1000 people (95% CI 15·9-24·7), unemployed people at 18·8 cases per 1000 people (16·2-21·4), those with no formal education at 18·5 cases per 1000 people (16·3-20·7), and adolescents aged 13-18 years at 15·2 cases per 1000 people (12·0-18·5). The epilepsy diagnostic gap was 80%. INTERPRETATION: Epilepsy is common in urban informal settlements of Nairobi, with large diagnostic gaps. Targeted interventions are needed to increase early epilepsy detection, particularly among vulnerable groups, to enable prompt treatment and prevention of adverse social consequences. FUNDING: National Institute for Health Research using Official Development Assistance

Development and validation of a diagnostic aid for convulsive epilepsy in sub-Saharan Africa: a retrospective case-control study

Background: Identification of convulsive epilepsy in sub-Saharan Africa relies on access to resources that are often unavailable. Infrastructure and resource requirements can further complicate case verification. Using machine-learning techniques, we have developed and tested a region-specific questionnaire panel and predictive model to identify people who have had a convulsive seizure. These findings have been implemented into a free app for health-care workers in Kenya, Uganda, Ghana, Tanzania, and South Africa. Methods: In this retrospective case-control study, we used data from the Studies of the Epidemiology of Epilepsy in Demographic Sites in Kenya, Uganda, Ghana, Tanzania, and South Africa. We randomly split these individuals using a 7:3 ratio into a training dataset and a validation dataset. We used information gain and correlation-based feature selection to identify eight binary features to predict convulsive seizures. We then assessed several machine-learning algorithms to create a multivariate prediction model. We validated the best-performing model with the internal dataset and a prospectively collected external-validation dataset. We additionally evaluated a leave-one-site-out model (LOSO), in which the model was trained on data from all sites except one that, in turn, formed the validation dataset. We used these features to develop a questionnaire-based predictive panel that we implemented into a multilingual app (the Epilepsy Diagnostic Companion) for health-care workers in each geographical region. Findings: We analysed epilepsy-specific data from 4097 people, of whom 1985 (48·5%) had convulsive epilepsy, and 2112 were controls. From 170 clinical variables, we initially identified 20 candidate predictor features. Eight features were removed, six because of negligible information gain and two following review by a panel of qualified neurologists. Correlation-based feature selection identified eight variables that demonstrated predictive value; all were associated with an increased risk of an epileptic convulsion except one. The logistic regression, support vector, and naive Bayes models performed similarly, outperforming the decision-tree model. We chose the logistic regression model for its interpretability and implementability. The area under the receiver operator curve (AUC) was 0·92 (95% CI 0·91–0·94, sensitivity 85·0%, specificity 93·7%) in the internal-validation dataset and 0·95 (0·92–0·98, sensitivity 97·5%, specificity 82·4%) in the external-validation dataset. Similar results were observed for the LOSO model (AUC 0·94, 0·93–0·96, sensitivity 88·2%, specificity 95·3%). Interpretation: On the basis of these findings, we developed the Epilepsy Diagnostic Companion as a predictive model and app offering a validated culture-specific and region-specific solution to confirm the diagnosis of a convulsive epileptic seizure in people with suspected epilepsy. The questionnaire panel is simple and accessible for health-care workers without specialist knowledge to administer. This tool can be iteratively updated and could lead to earlier, more accurate diagnosis of seizures and improve care for people with epilepsy

Focused cardiac ultrasound: Competency among pre‐internship medical officers in diagnosing cardiac causes of dyspnea

Background Differentiating cardiovascular causes of dyspnea in resource‐limited healthcare settings can be challenging. The use of easy‐to‐train, point‐of‐care, focused cardiac ultrasound (FoCUS) protocols may potentially alleviate this challenge. Research Question Can novices attain competency in FoCUS use after training using the cardiac ultrasound for resource‐limited settings (CURLS) protocol? Methods A quasi‐experimental study was conducted at the Kenyatta National Hospital in Nairobi, Kenya. Forty‐five graduate medical pre‐interns, novices in cardiac ultrasound, received simulated didactic and hands‐on FoCUS skills training using the CURLS protocol and 2018 European Association of Cardiovascular Imaging (EACVI) FoCUS training and competence assessment recommendations. Competency was assessed in image interpretation, image acquisition, and image quality. Results Aggregate image interpretation competency was attained by n = 38 (84%) of trainees with a median score of 80%. The proportion of trainees attaining category‐specific image interpretation competency was as follows: pericardial effusion n = 44 (98%), left atrial enlargement n = 40 (89%), cardiomyopathy n = 38 (84%), left ventricular hypertrophy n = 37 (82%), and right ventricular enlargement n = 29 (64%). Image acquisition skills competency was attained by n = 36 (80%) of trainees. Three‐quarters of trainee‐obtained images were of good quality. Conclusion Majority of the trainees attained competency. Training constraints limit the generalizability of our findings

Active convulsive epilepsy in a rural district of Kenya: a study of prevalence and possible risk factors.

BACKGROUND: Few large-scale studies of epilepsy have been done in sub-Saharan Africa. We aimed to estimate the prevalence of, treatment gap in, and possible risk factors for active convulsive epilepsy in Kenyan people aged 6 years or older living in a rural area. METHODS: We undertook a three-phase screening survey of 151,408 individuals followed by a nested community case-control study. Treatment gap was defined as the proportion of cases of active convulsive epilepsy without detectable amounts of antiepileptic drugs in blood. FINDINGS: Overall prevalence of active convulsive epilepsy was 2.9 per 1000 (95% CI 2.6-3.2); after adjustment for non-response and sensitivity, prevalence was 4.5 per 1000 (4.1-4.9). Substantial heterogeneity was noted in prevalence, with evidence of clustering. Treatment gap was 70.3% (65.9-74.5), with weak evidence of a difference by sex and area. Adjusted odds of active convulsive epilepsy for all individuals were increased with a family history of non-febrile convulsions (odds ratio 3.3, 95% CI 2.4-4.7; p<0.0001), family history of febrile convulsions (14.6, 6.3-34.1; p<0.0001), history of both seizure types (7.3, 3.3-16.4; p<0.0001), and previous head injury (4.1, 2.1-8.1; p<0.0001). Findings of multivariable analyses in children showed that adverse perinatal events (5.7, 2.6-12.7; p<0.0001) and the child's mother being a widow (5.1, 2.4-11.0; p<0.0001) raised the odds of active convulsive epilepsy. INTERPRETATION: Substantial heterogeneity exists in prevalence of active convulsive epilepsy in this rural area in Kenya. Assessment of prevalence, treatment use, and demographic variation in screening response helped to identify groups for targeted interventions. Adverse perinatal events, febrile illness, and head injury are potentially preventable associated factors for epilepsy in this region

Cognitive impairment in people living with HIV: consensus recommendations for a new approach

Current approaches to classifying cognitive impairment in people living with HIV can overestimate disease burden and lead to ambiguity around disease mechanisms. The 2007 criteria for HIV-associated neurocognitive disorders (HAND), sometimes called the Frascati criteria, can falsely classify over 20% of cognitively healthy individuals as having cognitive impairment. Minimum criteria for HAND are met on the basis of performance on cognitive tests alone, which might not be appropriate for populations with diverse educational and socioeconomic backgrounds. Imprecise phenotyping of cognitive impairment can limit mechanistic research, biomarker discovery and treatment trials. Importantly, overestimation of cognitive impairment carries the risk of creating fear among people living with HIV and worsening stigma and discrimination towards these individuals. To address this issue, we established the International HIV-Cognition Working Group, which is globally representative and involves the community of people living with HIV. We reached consensus on six recommendations towards a new approach for diagnosis and classification of cognitive impairment in people living with HIV, intended to focus discussion and debate going forward. We propose the conceptual separation of HIV-associated brain injury — including active or pretreatment legacy damage — from other causes of brain injury occurring in people living with HIV. We suggest moving away from a quantitative neuropsychological approach towards an emphasis on clinical context. Our recommendations are intended to better represent the changing profile of cognitive impairment in people living with HIV in diverse global settings and to provide a clearer framework of classification for clinical management and research studies

A new approach to cognitive impairment in people with HIV

The most frequently used criteria for cognitive impairment in people with HIV are the HIV–associated neurocognitive disorders (HAND) criteria, developed in 2007 by a working group formed by the US National Institute of Mental Health. The HAND criteria (sometimes referred to as the Frascati criteria) were intended for use in research, but the terminology has become widely used to refer to clinical burden of cognitive impairment in people with HIV across diverse settings globally

[Correspondence] Reply to ‘Cognitive criteria in HIV: greater consensus is needed’

We thank Cysique and colleagues for their considered response to our Consensus Statement (Nightingale, S. et al. Cognitive impairment in people living with HIV: consensus recommendations for a new approach. Nat. Rev. Neurol. 19, 424–433; 2023)1, and welcome discussion and debate on this important subject (Cysique, L. A. et al. Cognitive criteria in HIV: greater consensus is needed. Nat. Rev. Neurol. https://doi.org/10.1038/s41582-024-00927-1; 2024)2.</p