Using a hybrid agent-based and equation based model to test school closure policies during a measles outbreak.

BACKGROUND: In order to be prepared for an infectious disease outbreak it is important to know what interventions will or will not have an impact on reducing the outbreak. While some interventions might have a greater effect in mitigating an outbreak, others might only have a minor effect but all interventions will have a cost in implementation. Estimating the effectiveness of an intervention can be done using computational modelling. In particular, comparing the results of model runs with an intervention in place to control runs where no interventions were used can help to determine what interventions will have the greatest effect on an outbreak. METHODS: To test the effects of a school closure policy on the spread of an infectious disease (in this case measles) we run simulations closing schools based on either the proximity of the town to the initial outbreak or the centrality of the town within the network of towns in the simulation. To do this we use a hybrid model that combines an agent-based model with an equation-based model. In our analysis, we use three measures to compare the effects of different intervention strategies: the total number of model runs leading to an outbreak, the total number of infected agents, and the geographic spread of outbreaks. RESULTS: Our results show that closing down the schools in the town where an outbreak begins and the town with the highest in degree centrality provides the largest reduction in percent of runs leading to an outbreak as well as a reduction in the geographic spread of the outbreak compared to only closing down the town where the outbreak begins. Although closing down schools in the town with the closest proximity to the town where the outbreak begins also provides a reduction in the chance of an outbreak, we do not find the reduction to be as large as when the schools in the high in degree centrality town are closed. CONCLUSIONS: Thus we believe that focusing on high in degree centrality towns during an outbreak is important in reducing the overall size of an outbreak.

Examining the Limits of Predictability of Human Mobility.

We challenge the upper bound of human-mobility predictability that is widely used to corroborate the accuracy of mobility prediction models. We observe that extensions of recurrent-neural network architectures achieve significantly higher prediction accuracy, surpassing this upper bound. Given this discrepancy, the central objective of our work is to show that the methodology behind the estimation of the predictability upper bound is erroneous and identify the reasons behind this discrepancy. In order to explain this anomaly, we shed light on several underlying assumptions that have contributed to this bias. In particular, we highlight the consequences of the assumed Markovian nature of human-mobility on deriving this upper bound on maximum mobility predictability. By using several statistical tests on three real-world mobility datasets, we show that human mobility exhibits scale-invariant long-distance dependencies, contrasting with the initial Markovian assumption. We show that this assumption of exponential decay of information in mobility trajectories, coupled with the inadequate usage of encoding techniques results in entropy inflation, consequently lowering the upper bound on predictability. We highlight that the current upper bound computation methodology based on Fano's inequality tends to overlook the presence of long-range structural correlations inherent to mobility behaviors and we demonstrate its significance using an alternate encoding scheme. We further show the manifestation of not accounting for these dependencies by probing the mutual information decay in mobility trajectories. We expose the systematic bias that culminates into an inaccurate upper bound and further explain as to why the recurrent-neural architectures, designed to handle long-range structural correlations, surpass this upper limit on human mobility predictability.

A scoring system predicting the clinical course of CLPB defect based on the foetal and neonatal presentation of 31 patients.

Recently, CLPB deficiency has been shown to cause a genetic syndrome with cataracts, neutropenia, and 3-methylglutaconic aciduria. Surprisingly, the neurological presentation ranges from completely unaffected to patients with virtual absence of development. Muscular hypo- and hypertonia, movement disorder and progressive brain atrophy are frequently reported. We present the foetal, peri- and neonatal features of 31 patients, of which five are previously unreported, using a newly developed clinical severity scoring system rating the clinical, metabolic, imaging and other findings weighted by the age of onset. Our data are illustrated by foetal and neonatal videos. The patients were classified as having a mild (n = 4), moderate (n = 13) or severe (n = 14) disease phenotype. The most striking feature of the severe subtype was the neonatal absence of voluntary movements in combination with ventilator dependency and hyperexcitability. The foetal and neonatal presentation mirrored the course of disease with respect to survival (current median age 17.5 years in the mild group, median age of death 35 days in the severe group), severity and age of onset of all findings evaluated. CLPB deficiency should be considered in neonates with absence of voluntary movements, respiratory insufficiency and swallowing problems, especially if associated with 3-methylglutaconic aciduria, neutropenia and cataracts. Being an important differential diagnosis of hyperekplexia (exaggerated startle responses), we advise performing urinary organic acid analysis, blood cell counts and ophthalmological examination in these patients. The neonatal presentation of CLPB deficiency predicts the course of disease in later life, which is extremely important for counselling.

The first occurrence of a CTX-M ESBL-producing Escherichia coli outbreak mediated by mother to neonate transmission in an Irish neonatal intensive care unit.

BACKGROUND: Escherichia coli (E. coli) comprise part of the normal vaginal microflora. Transfer from mother to neonate can occur during delivery resulting, sometimes, in neonatal bacterial disease. Here, we aim to report the first outbreak of CTX-M ESBL-producing E. coli with evidence of mother-to-neonate transmission in an Irish neonatal intensive care unit (NICU) followed by patient-to-patient transmission. METHODS: Investigation including molecular typing was conducted. Infection was defined by clinical and laboratory criteria and requirement for antimicrobial therapy with or without positive blood cultures. Colonisation was determined by isolation without relevant symptoms or indicators of infection. RESULTS: Index case was an 8-day-old baby born at 34 weeks gestation who developed ESBL-producing E. coli infections at multiple body sites. Screening confirmed their mother as colonised with ESBL-producing E. coli. Five other neonates, in the NICU simultaneously with the index case, also tested positive. Of these, four were colonised while one neonate developed sepsis, requiring antimicrobial therapy. The second infected neonate's mother was also colonised by ESBL-producing E. coli. Isolates from all eight positive patients (6 neonates, 2 mothers) were compared using pulsed-field gel electrophoresis (PFGE). Two distinct ESBL-producing strains were implicated, with evidence of transmission between mothers and neonates for both strains. All isolates were confirmed as CTX-M ESBL-producers. There were no deaths associated with the outbreak. CONCLUSIONS: Resources were directed towards control interventions focused on hand hygiene and antimicrobial stewardship, which ultimately proved successful. Since this incident, all neonates admitted to the NICU have been screened for ESBL-producers and expectant mothers are screened at their first antenatal appointment. To date, there have been no further outbreaks.

Oronasopharyngeal suction versus wiping of the mouth and nose at birth: a randomised equivalency trial.

BACKGROUND: Wiping of the mouth and nose at birth is an alternative method to oronasopharyngeal suction in delivery-room management of neonates, but whether these methods have equivalent effectiveness is unclear. METHODS: For this randomised equivalency trial, neonates delivered at 35 weeks' gestation or later at the University of Alabama at Birmingham Hospital, Birmingham, AL, USA, between October, 2010, and November, 2011, were eligible. Before birth, neonates were randomly assigned gentle wiping of the face, mouth (implemented by the paediatric or obstetric resident), and nose with a towel (wipe group) or suction with a bulb syringe of the mouth and nostrils (suction group). The primary outcome was the respiratory rate in the first 24 h after birth. We hypothesised that respiratory rates would differ by fewer than 4 breaths per min between groups. Analysis was by intention to treat. This study is registered with ClinicalTrials.gov, number NCT01197807. FINDINGS: 506 neonates born at a median of 39 weeks' gestation (IQR 38-40) were randomised. Three parents withdrew consent and 15 non-vigorous neonates with meconium-stained amniotic fluid were excluded. Among the 488 treated neonates, the mean respiratory rates in the first 24 h were 51 (SD 8) breaths per min in the wipe group and 50 (6) breaths per min in the suction group (difference of means 1 breath per min, 95% CI -2 to 0, p<0·001). INTERPRETATION: Wiping the nose and mouth has equivalent efficacy to routine use of oronasopharyngeal suction in neonates born at or beyond 35 weeks' gestation. FUNDING: None.

Predictors of social risk for post-ischemic stroke reintegration.

After stroke rehabilitation, patients need to reintegrate back into their daily life, workplace and society. Reintegration involves complex processes depending on age, sex, stroke severity, cognitive, physical, as well as socioeconomic factors that impact long-term outcomes post-stroke. Moreover, post-stroke quality of life can be impacted by social risks of inadequate family, social, economic, housing and other supports needed by the patients. Social risks and barriers to successful reintegration are poorly understood yet critical for informing clinical or social interventions. Therefore, the aim of this work is to predict social risk at rehabilitation discharge using sociodemographic and clinical variables at rehabilitation admission and identify factors that contribute to this risk. A Gradient Boosting modelling methodology based on decision trees was applied to a Catalan 217-patient cohort of mostly young (mean age 52.7), male (66.4%), ischemic stroke survivors. The modelling task was to predict an individual's social risk upon discharge from rehabilitation based on 16 different demographic, diagnostic and social risk variables (family support, social support, economic status, cohabitation and home accessibility at admission). To correct for imbalance in patient sample numbers with high and low-risk levels (prediction target), five different datasets were prepared by varying the data subsampling methodology. For each of the five datasets a prediction model was trained and the analysis involves a comparison across these models. The training and validation results indicated that the models corrected for prediction target imbalance have similarly good performance (AUC 0.831-0.843) and validation (AUC 0.881 - 0.909). Furthermore, predictor variable importance ranked social support and economic status as the most important variables with the greatest contribution to social risk prediction, however, sex and age had a lesser, but still important, contribution. Due to the complex and multifactorial nature of social risk, factors in combination, including social support and economic status, drive social risk for individuals.

Gender and sex hormone effects on neonatal innate immune function.

OBJECTIVES: Scientific evidence provides a widened view of differences in immune response between male and female neonates. The X-chromosome codes for several genes important in the innate immune response and neonatal innate immune cells express receptors for, and are inhibited by, maternal sex hormones. We hypothesized that sex differences in innate immune responses may be present in the neonatal population which may contribute to the increased susceptibility of premature males to sepsis. We aimed to examine the in vitro effect of pro-inflammatory stimuli and hormones in neutrophils and monocytes of male and female neonates, to examine the expression of X-linked genes involved in innate immunity and the miRNA profiles in these populations. METHODS: Preterm infants (n = 21) and term control (n = 19) infants were recruited from the Coombe Women and Infants University Hospital Dublin with ethical approval and explicit consent. The preterm neonates (eight female, 13 male) were recruited with a mean gestation at birth (mean ± SD) of 28 ± 2 weeks and corrected gestation at the time of sampling was 30 + 2.6 weeks. The mean birth weight of preterm neonates was 1084 ± 246 g. Peripheral blood samples were used to analyze immune cell phenotypes, miRNA human panel, and RNA profiles for inflammasome and inflammatory genes. RESULTS: Dividing neutrophil results by sex showed no differences in baseline CD11b between sexes among either term or preterm neonates. Examining monocyte CD11b by sex shows, that at baseline, total and classical monocytes have higher CD11b in preterm females than preterm males. Neutrophil TLR2 did not differ between sexes at baseline or following lipopolysaccharide (LPS) exposure. CD11b expression was higher in preterm male non-classical monocytes following Pam3CSK treatment when compared to females, a finding which is unique to our study. Preterm neonates had higher TLR2 expression at baseline in total monocytes, classical monocytes and non-classical monocytes than term. A sex difference was evident between preterm females and term females in TLR2 expression only. Hormone treatment showed no sex differences and there was no detectable difference between males and females in X-linked gene expression. Two miRNAs, miR-212-3p and miR-218-2-3p had significantly higher expression in preterm female than preterm male neonates. CONCLUSIONS: This study examined immune cell phenotypes and x-linked gene expression in preterm neonates and stratified according to gender. Our findings suggest that the responses of females mature with advancing gestation, whereas male term and preterm neonates have very similar responses. Female preterm neonates have improved monocyte activation than males, which likely reflects improved innate immune function as reflected clinically by their lower risk of sepsis. Dividing results by sex showed changes in preterm and term infants at baseline and following LPS stimulation, a difference which is reflected clinically by infection susceptibility. The sex difference noted is novel and may be limited to the preterm or early neonatal population as TLR2 expression on monocytes of older children does not differ between males and females. The differences shown in female and male innate immune cells likely reflect a superior innate immune defense system in females with sex differences in immune cell maturation. Existing human studies on sex differences in miRNA expression do not include preterm patients, and most frequently use either adult blood or cord blood. Our findings suggest that miRNA profiles are similar in neonates of opposite sexes at term but require further investigation in the preterm population. Our findings, while novel, provide only very limited insights into sex differences in infection susceptibility in the preterm population leaving many areas that require further study. These represent important areas for ongoing clinical and laboratory study and our findings represent an important contribution to exiting literature.

Computable phenotype for real-world, data-driven retrospective identification of relapse in ANCA-associated vasculitis.

OBJECTIVE: ANCA-associated vasculitis (AAV) is a relapsing-remitting disease, resulting in incremental tissue injury. The gold-standard relapse definition (Birmingham Vasculitis Activity Score, BVAS>0) is often missing or inaccurate in registry settings, leading to errors in ascertainment of this key outcome. We sought to create a computable phenotype (CP) to automate retrospective identification of relapse using real-world data in the research setting. METHODS: We studied 536 patients with AAV and >6 months follow-up recruited to the Rare Kidney Disease registry (a national longitudinal, multicentre cohort study). We followed five steps: (1) independent encounter adjudication using primary medical records to assign the ground truth, (2) selection of data elements (DEs), (3) CP development using multilevel regression modelling, (4) internal validation and (5) development of additional models to handle missingness. Cut-points were determined by maximising the F1-score. We developed a web application for CP implementation, which outputs an individualised probability of relapse. RESULTS: Development and validation datasets comprised 1209 and 377 encounters, respectively. After classifying encounters with diagnostic histopathology as relapse, we identified five key DEs; DE1: change in ANCA level, DE2: suggestive blood/urine tests, DE3: suggestive imaging, DE4: immunosuppression status, DE5: immunosuppression change. F1-score, sensitivity and specificity were 0.85 (95% CI 0.77 to 0.92), 0.89 (95% CI 0.80 to 0.99) and 0.96 (95% CI 0.93 to 0.99), respectively. Where DE5 was missing, DE2 plus either DE1/DE3 were required to match the accuracy of BVAS. CONCLUSIONS: This CP accurately quantifies the individualised probability of relapse in AAV retrospectively, using objective, readily accessible registry data. This framework could be leveraged for other outcomes and relapsing diseases.

Determining the Proportionality of Ischemic Stroke Risk Factors to Age.

While age is an important risk factor, there are some disadvantages to including it in a stroke risk model: age can dominate the risk score and lead to over- or under-predictions in some age groups. There is evidence to suggest that some of these disadvantages are due to the non-proportionality of other risk factors with age, e.g., risk factors contribute differently to stroke risk based on an individual's age. In this paper, we present a framework to test if risk factors are proportional with age. We then apply the framework to a set of risk factors using Framingham heart study data from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center to determine if we can find evidence of non-proportionality. Using our framework, we find that a number of risk factors (diastolic blood pressure, total cholesterol, BMI, sex, high blood pressure treatment) may be non-proportional to age. This suggests that testing for the proportionality of risk factors with age should be something that is considered in stroke risk prediction modelling and traditional modelling methods may need to be adjusted to capture this non-proportionality.

Current Topics in Technology-Enabled Stroke Rehabilitation and Reintegration: A Scoping Review and Content Analysis.

BACKGROUND: There is a worldwide health crisis stemming from the rising incidence of various debilitating chronic diseases, with stroke as a leading contributor. Chronic stroke management encompasses rehabilitation and reintegration, and can require decades of personalized medicine and care. Information technology (IT) tools have the potential to support individuals managing chronic stroke symptoms. OBJECTIVES: This scoping review identifies prevalent topics and concepts in research literature on IT technology for stroke rehabilitation and reintegration, utilizing content analysis, based on topic modelling techniques from natural language processing to identify gaps in this literature. ELIGIBILITY CRITERIA: Our methodological search initially identified over 14,000 publications of the last two decades in the Web of Science and Scopus databases, which we filter, using keywords and a qualitative review, to a core corpus of 1062 documents. RESULTS: We generate a 3-topic, 4-topic and 5-topic model and interpret the resulting topics as four distinct thematics in the literature, which we label as Robotics, Software, Functional and Cognitive. We analyze the prevalence and distinctiveness of each thematic and identify some areas relatively neglected by the field. These are mainly in the Cognitive thematic, especially for systems and devices for sensory loss rehabilitation, tasks of daily living performance and social participation. CONCLUSION: The results indicate that IT-enabled stroke literature has focused on Functional outcomes and Robotic technologies, with lesser emphasis on Cognitive outcomes and combined interventions. We hope this review broadens awareness, usage and mainstream acceptance of novel technologies in rehabilitation and reintegration among clinicians, carers and patients.

Delivery room glucose to reduce the risk of admission hypoglycemia in preterm infants: a systematic literature review.

AIMS: In order to mitigate early hypoglycemia in preterm infants, some clinicians have recently explored interventions such as delivery room commencement of dextrose infusions or delivery room administration of buccal dextrose gel. This review aimed to systematically investigate the literature regarding the provision of delivery room (prior to admission) parenteral glucose as a method to reduce the risk of initial hypoglycemia (measured at the time of NICU admission blood testing) in preterm infants. MATERIALS AND METHODS: Using PRISMA guidelines a literature search (May 2022) was conducted using PubMed, Embase, Scopus, Cochrane Library, OpenGrey, and Prospero databases. The clinicaltrials.gov database was searched for possible completed/ongoing clinical trials. Studies that included moderate preterm (≤33+6 weeks) or younger birth gestations or very low birth weight (or smaller) infants, and that administered parenteral glucose in the delivery room were included. The literature was appraised via data extraction, narrative synthesis, and critical review of the study data. RESULTS: A total of five studies (published 2014-2022) were eligible for inclusion (three before-after "quasi-experimental" studies, one retrospective cohort study, and one case-control study). Most included studies used intravenous dextrose as the intervention. Individual study effects (odds ratios) favored the intervention in all included studies. It was felt that the low number of studies, the variability in study design, and the nonadjustment for confounding co-interventions (co-exposures) precluded a meta-analysis. Quality assessment of the studies revealed a spectrum of bias from low to high risk, however, most studies had moderate to high risk of bias, and their direction of bias favored the intervention. CONCLUSIONS: This extensive search and systematic appraisal of the literature indicates that there exists few studies (these are low grade and at moderate to high risk of bias) for the interventions of either intravenous or buccal dextrose given in the delivery room. It is not clear if these interventions impact on rates of early (NICU admission) hypoglycemia in these preterm infants. Obtaining intravenous access in the delivery room is not guaranteed and can be difficult in these small infants. Future research should consider various routes for commencing delivery room glucose in these preterm infants and should take the form of randomized controlled trials.

Telemedicine in a spine clinic setting: a large survey of patient preferences and experiences.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has greatly disturbed healthcare and the practice of neurosurgery. As healthcare systems evolve in the face of COVID-19, the use of telehealth platforms has expanded. We presented the results of a large survey of patient perspectives on the role of telehealth in the care of spine clinic patients. METHODS: All patients at the spine clinic of a large, tertiary, academic medical center were surveyed at their clinic visit from May 30, 2020-June 30, 2020. All responses were anonymous, and results were analyzed with standard statistical techniques. RESULTS: One hundred and seventy-six surveys were returned and 164 were entirely completed. 56.8% of patients were new while 24.4% were postoperative follow-up visits: 54.9% had lumbar symptoms. 85% had pain while more than half also reported weakness; 58% traveled greater than twenty-five miles for their appointment; 96% had transportation readily available. Of all respondents, only 15.3% preferred the appointment via video telehealth while 4% had no preference between in-person appointment or virtual visit. Preference for telehealth appointment was not associated with factors such as new or established in the practice, spinal region of symptoms, pain, weakness, comfort with technology, age, or duration of symptoms. There was a significant difference between how far the patient traveled for the clinic appointment and their preference for a telehealth appointment, with patients traveling further distances favoring telehealth (P=0.04). This effect remained significant when stratifying based on 25 miles (P=0.03) or 50 miles (P=0.03) but not when stratifying based on 100 miles (P=0.32). However, the sample size of patients traveling >100 miles was small, limiting any inference regarding that subgroup. Access to transportation was associated with preference for telehealth (88.89% vs. 97.18%, P=0.08) but did not reach statistical significance. CONCLUSIONS: Most spine patients prefer in-person clinic appointments to virtual appointments. These preferences should be considered when arranging patient encounters.

Long-term trajectories of community integration: identification, characterization, and prediction using inpatient rehabilitation variables.

BACKGROUND: Community integration (CI) is often regarded as the foundation of rehabilitation endeavors after stroke; nevertheless, few studies have investigated the relationship between inpatient rehabilitation (clinical and demographic) variables and long-term CI. OBJECTIVES: To identify novel classes of patients having similar temporal patterns in CI and relate them to baseline features. METHODS: Retrospective observational cohort study analyzing (n = 287) adult patients with stroke admitted to rehabilitation between 2003 and 2018, including baseline Functional Independence Measure (FIM) at discharge, follow-ups (m = 1264) of Community Integration Questionnaire (CIQ) between 2006 and 2022. Growth mixture models (GMMs) were fitted to identify CI trajectories, and baseline predictors were identified using multivariate logistic regression (reporting AUC) with 10-fold cross validation. RESULTS: Each patient was assessed at 2.7 (2.2-3.7), 4.4 (3.7-5.6), and 6.2 (5.4-7.4) years after injury, 66% had a fourth assessment at 7.9 (6.8-8.9) years. GMM identified three classes of trajectories.Lowest CI (n=105, 36.6%): The lowest mean total CIQ; highest proportion of dysphagia (47.6%) and aphasia (46.7%), oldest at injury, largest length of stay (LOS), largest time to admission, and lowest FIM.Highest CI (n=63, 21.9%): The highest mean total CIQ, youngest, shortest LOS, highest education (27% university) highest FIM, and Intermediate CI (n=119, 41.5%): Intermediate mean total CIQ and FIM scores. Age at injury OR: 0.89 (0.85-0.93), FIM OR: 1.04 (1.02-1.07), hypertension OR: 2.86 (1.25-6.87), LOS OR: 0.98 (0.97-0.99), and high education OR: 3.05 (1.22-7.65) predicted highest CI, and AUC was 0.84 (0.76-0.93). CONCLUSION: Novel clinical (e.g. hypertension) and demographic (e.g. education) variables characterized and predicted long-term CI trajectories.

Age Specific Models to Capture the Change in Risk Factor Contribution by Age to Short Term Primary Ischemic Stroke Risk.

Age is one of the most important risk factors when it comes to stroke risk prediction. However, including age as a risk factor in a stroke prediction model can give rise to a number of difficulties. Age often dominates the risk score, and also not all risk factors contribute proportionally to stroke risk by age. In this study we investigate a number of common stroke risk factors, using Framingham heart study data from the NHLBI Biologic Specimen and Data Repository Information Coordinating Center to determine if they appear to contribute proportionally by age to a stroke risk score. As we find evidence that there is some non-proportionality by age, we then create a set of logistic regression risk models that each predict the 5 year stroke risk for a different age group. The age group models are shown to be better calibrated when compared to a model for all ages that includes age as a risk factor. This suggests that to get better predictions for stroke risk it may be necessary to consider alternative methods for including age in stroke risk prediction models that account for the non-proportionality of the other risk factors as age changes.

A review of risk concepts and models for predicting the risk of primary stroke.

Predicting an individual's risk of primary stroke is an important tool that can help to lower the burden of stroke for both the individual and society. There are a number of risk models and risk scores in existence but no review or classification designed to help the reader better understand how models differ and the reasoning behind these differences. In this paper we review the existing literature on primary stroke risk prediction models. From our literature review we identify key similarities and differences in the existing models. We find that models can differ in a number of ways, including the event type, the type of analysis, the model type and the time horizon. Based on these similarities and differences we have created a set of questions and a system to help answer those questions that modelers and readers alike can use to help classify and better understand the existing models as well as help to make necessary decisions when creating a new model.

Shapley Idioms: Analysing BERT Sentence Embeddings for General Idiom Token Identification.

This article examines the basis of Natural Language Understanding of transformer based language models, such as BERT. It does this through a case study on idiom token classification. We use idiom token identification as a basis for our analysis because of the variety of information types that have previously been explored in the literature for this task, including: topic, lexical, and syntactic features. This variety of relevant information types means that the task of idiom token identification enables us to explore the forms of linguistic information that a BERT language model captures and encodes in its representations. The core of this article presents three experiments. The first experiment analyzes the effectiveness of BERT sentence embeddings for creating a general idiom token identification model and the results indicate that the BERT sentence embeddings outperform Skip-Thought. In the second and third experiment we use the game theory concept of Shapley Values to rank the usefulness of individual idiomatic expressions for model training and use this ranking to analyse the type of information that the model finds useful. We find that a combination of idiom-intrinsic and topic-based properties contribute to an expression's usefulness in idiom token identification. Overall our results indicate that BERT efficiently encodes a variety of information from topic, through lexical and syntactic information. Based on these results we argue that notwithstanding recent criticisms of language model based semantics, the ability of BERT to efficiently encode a variety of linguistic information types does represent a significant step forward in natural language understanding.

Anxiety as a Risk Factor for Postoperative Delirium in Elective Spine Deformity Surgeries: A National Database Study.

Introduction Generalized anxiety disorder has become one of the most prevalent mental health disorders in the United States. In addition, postoperative delirium has been shown to increase hospital stay, increase mortality, and increased healthcare costs. Few studies have looked at the prevalence of postoperative delirium in patients diagnosed with anxiety undergoing elective spinal deformity procedures. The purpose of this study was to determine if anxiety is a risk factor for postoperative delirium in elective spinal deformity surgeries. Methods The authors performed a retrospective analysis using the TriNetX Research Database. Patients diagnosed with kyphosis or lordosis who then underwent elective spinal correction surgeries were identified. This group was then separated based on the diagnosis of a generalized anxiety disorder before the operation versus no diagnosis. Propensity score adjustment, based on mental disorders and other risk factors, was then used to match cohorts on baseline demographics and characteristics. Analysis was performed on the primary outcome of postoperative delirium, with secondary outcomes of upper respiratory tract infections, surgical site infections, sepsis, ventilator dependence, convulsions, stroke, emergency department visits, myocardial infarction, pulmonary embolism, and urinary retention within 30 days after surgery. Results Our search included 1,211 patients with a diagnosis of anxiety and 8,055 patients without anxiety. After propensity score matching, 996 patients remained in each cohort. Statistical analysis showed significant outcomes between the matched cohorts in the anxiety group for postoperative delirium (OR 2.788; 1.587-4.899) and convulsions (OR 1.615; 1.006-2.592). All other outcomes were not significant after propensity score matching. Conclusion These results showed generalized anxiety disorder is a risk factor for postoperative delirium and convulsions after elective spine surgery. Further research is necessary on the effects of mental health disorders on postoperative delirium and other outcomes to better understand the risks in this population.

Understanding and Predicting Cognitive Improvement of Young Adults in Ischemic Stroke Rehabilitation Therapy.

Accurate early predictions of a patient's likely cognitive improvement as a result of a stroke rehabilitation programme can assist clinicians in assembling more effective therapeutic programs. In addition, sufficient levels of explainability, which can justify these predictions, are a crucial requirement, as reported by clinicians. This article presents a machine learning (ML) prediction model targeting cognitive improvement after therapy for stroke surviving patients. The prediction model relies on electronic health records from 201 ischemic stroke surviving patients containing demographic information, cognitive assessments at admission from 24 different standardized neuropsychology tests (e.g., TMT, WAIS-III, Stroop, RAVLT, etc.), and therapy information collected during rehabilitation (72,002 entries collected between March 2007 and September 2019). The study population covered young-adult patients with a mean age of 49.51 years and only 4.47% above 65 years of age at the stroke event (no age filter applied). Twenty different classification algorithms (from Python's Scikit-learn library) are trained and evaluated, varying their hyper-parameters and the number of features received as input. Best-performing models reported Recall scores around 0.7 and F1 scores of 0.6, showing the model's ability to identify patients with poor cognitive improvement. The study includes a detailed feature importance report that helps interpret the model's inner decision workings and exposes the most influential factors in the cognitive improvement prediction. The study showed that certain therapy variables (e.g., the proportion of memory and orientation executed tasks) had an important influence on the final prediction of the cognitive improvement of patients at individual and population levels. This type of evidence can serve clinicians in adjusting the therapeutic settings (e.g., type and load of therapy activities) and selecting the one that maximizes cognitive improvement.

Long-term trajectories of motor functional independence after ischemic stroke in young adults: Identification and characterization using inpatient baseline assessments.

BACKGROUND: Stroke is a major worldwide cause of serious long-term disability. Most previous studies addressing functional independence included only inpatients with limited follow-up. OBJECTIVE: To identify novel classes of patients having similar temporal patterns in motor functional independence and relate them to baseline clinical features. METHODS: Retrospective observational cohort study, data were obtained for n = 428 adult patients with ischemic stroke admitted to rehabilitation (March 2005-March 2020), including baseline clinical features and follow-ups of motor Functional Independence Measure (mFIM) categorized as poor, fair or good. Growth mixture models (GMMs) were fitted to identify classes of patients with similar mFIM trajectories. RESULTS: GMM identified three classes of trajectories (1,664 mFIM assessments):C1 (11.2 %), 97.9% having poor admission mFIM, at 4.93 years 61.1% still poor, with the largest percentage of hypertension, neglect, dysphagia, diabetes and dyslipidemia of all three classes.C2 (23.1%), 99% had poor admission mFIM, 25% poor discharge mFIM, the largest percentage of aphasia and greatest mFIM gain, at 4.93 years only 6.2% still poor.C3 (65.7%) the youngest, lowest NIHSS, 37.7% poor admission mFIM, 73% good discharge mFIM, only 4.6% poor discharge mFIM, 90% good at 4.93 years. CONCLUSIONS: GMM identified novel motor functional classes characterized by baseline features.

The association between ambient UVB dose and ANCA-associated vasculitis relapse and onset.

BACKGROUND: The aetiology of ANCA-associated vasculitis (AAV) and triggers of relapse are poorly understood. Vitamin D (vitD) is an important immunomodulator, potentially responsible for the observed latitudinal differences between granulomatous and non-granulomatous AAV phenotypes. A narrow ultraviolet B spectrum induces vitD synthesis (vitD-UVB) via the skin. We hypothesised that prolonged periods of low ambient UVB (and by extension vitD deficiency) are associated with the granulomatous form of the disease and an increased risk of AAV relapse. METHODS: Patients with AAV recruited to the Irish Rare Kidney Disease (RKD) (n = 439) and UKIVAS (n = 1961) registries were studied. Exposure variables comprised latitude and measures of ambient vitD-UVB, including cumulative weighted UVB dose (CW-D-UVB), a well-validated vitD proxy. An n-of-1 study design was used to examine the relapse risk using only the RKD dataset. Multi-level models and logistic regression were used to examine the effect of predictors on AAV relapse risk, phenotype and serotype. RESULTS: Residential latitude was positively correlated (OR 1.41, 95% CI 1.14-1.74, p = 0.002) and average vitD-UVB negatively correlated (0.82, 0.70-0.99, p = 0.04) with relapse risk, with a stronger effect when restricting to winter measurements (0.71, 0.57-0.89, p = 0.002). However, these associations were not restricted to granulomatous phenotypes. We observed no clear relationship between latitude, vitD-UVB or CW-D-UVB and AAV phenotype or serotype. CONCLUSION: Our findings suggest that low winter ambient UVB and prolonged vitD status contribute to AAV relapse risk across all phenotypes. However, the development of a granulomatous phenotype does not appear to be directly vitD-mediated. Further research is needed to determine whether sufficient vitD status would reduce relapse propensity in AAV.