Single Predictor Models Research Articles

Timing of stroke survivors' hospital readmissions to guide APRNs in primary care.

Caring for patients after a neurovascular incident is common for advanced practice registered nurses (APRNs). Most neurological readmission studies focus on a small subset of neurovascular incident groups, but advanced practice nurses in primary care attend to a diverse neurovascular population and lack time to adequately search hospital records. The aim of this study was to determine readmission risk factors after a neurovascular incident to guide APRNs in the primary care setting. The study is a retrospective observational study that used a crude single predictor model to determine potential risks for readmission. A total of 876 neurovascular participants were studied. Of these, only 317 experienced at least one hospital readmission, with 703 readmissions within 1 year, indicating some were readmitted more than once. Risks for readmission varied across neurovascular events. The main reasons for readmission were because of neurological, cardiovascular, and musculoskeletal complications. Stroke readmission rates are high and require intervention by APRNs. To prevent readmission includes timely follow-up within 30 days and should also include longitudinal follow-up beyond 90 days to prevent hospital readmission. Future studies are needed to create guidelines for APRNs that implement rehabilitation strategies to decrease hospital readmission for the neurovascular population that focus on interdisciplinary communication.

Quantifying Predictive Uncertainty and Feature Selection in River Bed Load Estimation: A Multi-Model Machine Learning Approach with Particle Swarm Optimization

This study presents a comprehensive multi-model machine learning (ML) approach to predict river bed load, addressing the challenge of quantifying predictive uncertainty in fluvial geomorphology. Six ML models—random forest (RF), categorical boosting (CAT), extra tree regression (ETR), gradient boosting machine (GBM), Bayesian regression model (BRM), and K-nearest neighbors (KNNs)—were thoroughly evaluated across several performance metrics like root mean square error (RMSE), and correlation coefficient (R). To enhance model training and optimize performance, particle swarm optimization (PSO) was employed for hyperparameter tuning across all the models, leveraging its capability to efficiently explore complex hyperparameter spaces. Our findings indicated that RF, GBM, CAT, and ETR demonstrate superior predictive performance (R score > 0.936), benefiting significantly from PSO. In contrast, BRM displayed lower performance (0.838), indicating challenges with Bayesian approaches. The feature importance analysis, including permutation feature and SHAP values, highlighted the non-linear interdependencies between the variables, with river discharge (Q), bed slope (S), and flow width (W) being the most influential. This study also examined the specific impact of individual variables on model performance by adding and excluding individual variables, which is particularly meaningful when choosing input variables for the model, especially in limited data conditions. Uncertainty quantification through Monte Carlo simulations highlighted the enhanced predictability and reliability of models with larger datasets. The correlation between increased training data and improved model precision was evident in the consistent rise in mean R scores and reduction in standard deviations as the sample size increased. This research underscored the potential of advanced ensemble methods and PSO to mitigate the limitations of single-predictor models and exploit collective model strengths, thereby improving the reliability of predictions in river bed load estimation. The insights from this study provide a valuable framework for future research directions focused on optimizing ensemble configurations for hydro-dynamic modeling.

Effect of Texas Abortion Laws on Reproductive Health Attitudes Among Medical Students, Residents, and Faculty: A TTUHSC Study [ID 2683579

INTRODUCTION: In response to Texas Senate Bill 8 and the overturning of Roe v. Wade, we investigated the effect of new abortion laws on attitudes toward reproductive health and abortion within the TTUHSC community in Lubbock, Texas. As abortion remains a vital aspect of reproductive health care, understanding current and future doctors’ sentiments is essential. METHODS: The Omnibus survey was sent electronically to TTUHSC School of Medicine students as well as medical residents, fellows, and faculty. The survey included 25 question sets that branched according to respondent groups and received 318 responses from the TTUHSC community. Using a single predictor model in RStudio, we analyzed gender influence on attitudes toward post-law changes. All participants provided consent for their responses to be used for research purposes. This project was approved for exempt review by the TTUHSC IRB. RESULTS: Of 76 responses, 63% strongly agreed that medical education should address Texas abortion law changes. Females showed more desire (73%) for education changes (P=.0059). Gender also affected opinions on counseling limitations due to new laws (P=.0167) and obstetrics and gynecology residents’ abortion training (P=.0063). CONCLUSION: Most participants favored educating students about law changes, perceived counseling limitations, and obstetrics and gynecology training for abortions. Females consistently aligned with these sentiments. The gender-based disparities underscore a medical education gap exacerbated by the new laws. Ambiguities in current laws may affect clinical practice. Addressing these findings is crucial and encompasses integrating ethical considerations, legal nuances, and procedures into the medical education framework to produce competent, compassionate practitioners.

100 Can in vitro or in situ disappearance predict to in vivo digestibility of forages?

Abstract Though much effort has been made to characterize the in vivo (IV) and in situ (IS) dry matter digestibility (DMD) of southeastern forages, there is a lack of information identifying methods that best represent the in vivo digestibility of these forages. Thus, the objective of our study was to compare IV and IS methods to determine which technique best represents the in vivo DMD of four bermudagrass (Cynodon dactylon [L.] Pers.) cultivars. In an IV experiment, ruminally fistulated heifers (n = 4) were randomly assigned to one of four bermudagrass cultivars (Coastal [COS], Russell [RUS], Tifton 44 [T44], or Tifton 85 [T85]) for four 30-d in vivo periods (21-d adaptation and 9-d collection) in a Latin square design. On d 28 of each period, rumen fluid was collected 4 h post-feeding for an accompanying IV experiment using a completely randomized design with a 4 × 2 factorial treatment structure. Factors in the IV experiment included bermudagrass cultivar (COS, RUS, T44, and T85) and digestibility method (Tilley and Terry [TT] or Goering and Van Soest [GVS]). On d 31 of in vivo periods 3 and 5, an accompanying IS experiment was conducted as a randomized complete block design with three treatment factors (in vivo diet [previous described], bermudagrass cultivar [previously described], and incubation timepoint [0, 0.25, 0.50, 0.75, 1, 2, 3, 4, 8, 12, 16, 20, 24, 48, 72, 96, 120, 144, and 168 h]). Correlations were computed between in vivo DM disappearance and each of the IV (TT and GVS) and IS (24, 48, and 72 h) methods. Regressions were calculated for each of the individual IV and IS measurements, and a stepwise regression was used to determine the predictive value of linear combinations of the measurements. None of the IV or IS DMD values were correlated with in vivo DMD (P ≥ 0.18). Individual IV and IS DMD values did not yield significant models through linear regression (P ≥ 0.18), though the best model (based on AIC) was TT IVDMD (r2 = 0.09; P = 0.26). Stepwise regression revealed that there was no linear combination of IV or IS DMD that improved prediction beyond single predictor models. While both IV and IS experiments remain viable tools to screen forages and make relative comparisons, results from this experiment are interpreted to mean that neither of these methods is suitable for prediction of in vivo performance.

Modelling urban freight generation using linear regression and proportional odds logit models

Receivers and intermediary establishments typical to urban areas have unique freight behaviour and are understudied, particularly in developing countries. This paper uses establishment-based freight survey data to examine their freight generation patterns in the Chennai metropolitan area, India. Three sets of Simple Linear Regression (SLR) models, three single predictor proportional odds logit models, one multiple linear regression model, and one multiple predictor proportional odds logit model with business size (employment, area, and operational age) and indicators of establishment category as regressors are developed each for both freight production and freight attraction. Partial proportional odds logit models are developed in a few cases to overcome the limitations of proportional odds logit models. The best SLR model varied with the establishment category for freight production and attraction. The establishment area model is the best among single predictor proportional odds logit models. The multiple predictor proportional odds logit models marginally improved the fit over single predictor models. The proportional odds logit model results show that the establishment category has a greater impact on freight generation levels than business size variables. Since earlier studies rarely focused on receivers and intermediary establishments, policymakers may benefit from the developed models and study insights while estimating freight demand and developing freight policies.

Incidence of Suicidal Ideation in a Civil Servants Cohort During the COVID-19 Pandemic in Brazil: Insights from the ELSA-Brasil Study.

This study investigated the incidence of suicidal ideation and its associated risk factors in the São Paulo state of ELSA-Brasil cohort during the COVID-19 pandemic. During a pre-pandemic ELSA-Brasil onsite assessment in 2016-2018 (wave 3) and a pandemic online assessment in May-July 2020 (wave COVID), we assessed suicidal ideation using the Clinical Interview Scheduled-Revised (CIS-R). Single and multi predictor logistic regressions were performed using sociodemographic characteristics, household finance impact during pandemic, presence of previous chronic diseases, alcohol abuse, adverse childhood experiences (ACE), living alone, and previous CMD as predictors. Suicidal ideation incidence was used as outcome. Out of 4191 participants of wave 3, 2117 (50.5%) answered wave COVID. There was a threefold increase in suicide ideation, from 34 (1.8%) to 104 (5.6%).In multiple predictor models, we found that previous CMD (OR 7.17; 95% CI 4.43 - 11.58) and ACE (OR 1.72; 95% CI 1.09 - 2.72) increased the odds of incident suicidal ideation. The sociodemographic predictors female sex, younger age and low income were significant risk factors only in the single predictor model. Conclusions These findings underscore the importance of monitoring and supporting individuals who suffered ACE and have a history of mental health disorders. This is especially critical in times of heightened societal stress, such as the COVID-19 pandemic. These findings underscore the importance of monitoring and supporting individuals who suffered ACE and have a history of mental health disorders. This is especially critical in times of heightened societal stress, such as the COVID-19 pandemic.

The role of co-speech gestures in retrieval and prediction during naturalistic multimodal narrative processing

ABSTRACT During daily communication, visual cues such as gestures accompany the speech signal and facilitate semantic processing. However, how gestures impact lexical retrieval and semantic prediction, especially in a naturalistic setting, remains unclear. Here, participants watched a naturalistic multimodal narrative, where an actor narrated a story and spontaneously produced co-speech gestures. For all content words, word frequency and lexical surprisal were regressed against the EEG using temporal response functions (TFRs), which were fitted separately, additively, and interactively for words accompanied and not accompanied by gestures. Results from our analyses suggest a robust modulation effect of gesture on the frequency-dependent regression N400. Besides, we also observed some evidence of modulative effect of gesture on the surprisal-N400 effect based on the single-predictor model. Our finding thus suggests that, on a neural level, the presence of co-speech gestures facilitates lexical retrieval and potentially semantic prediction during the processing of naturalistic multimodal stimuli.

Combining plasma p‐tau231 and glial fibrillary acidic protein produces higher discriminative accuracy for amyloid positivity than other blood‐based biomarker combinations

AbstractBackgroundPlasma phosphorylated tau (p‐tau)231 is an emerging blood‐based biomarker that has shown great promise for early identification of individuals at risk of Alzheimer’s disease (AD). However, it is unclear if this biomarker is sufficient as a standalone test or if diagnostic classification improves when simultaneously considering other plasma biomarkers. We investigated various plasma biomarkers, individually and in combination, in relation to cerebrospinal fluid (CSF) amyloid positivity.MethodVanderbilt Memory and Aging Project participants free of clinical dementia and stroke (n = 155, 72±6 years, 33% female) underwent fasting lumbar puncture and blood draw. CSF β‐amyloid42 (Aβ42) and β‐amyloid40 (Ab40) and plasma p‐tau231, glial fibrillary acidic protein (GFAP), Aβ42, Aβ40, and neurofilament light chain (NfL) were analyzed in batch. The Aβ42/40 ratio was used for all analyses and CSF amyloid positivity was defined as Aβ42/40 ratio <0.072. Logistic regression models related each biomarker to amyloid positivity, adjusting for age, sex, race/ethnicity, apolipoprotein E‐ε4 status, and cognitive status. Subsequent models included p‐tau231 as the predictor in a base model with other biomarkers being added individually and then in combination with other biomarkers to the base model. Models were compared using likelihood ratio test (LRTest) and Akaike Information Criterion (AIC) values.ResultIn single predictor models, p‐tau231 (C‐index = 0.87, p = 0.005) and GFAP (C‐index = 0.87, p = 0.01) were associated with amyloid positivity, while other biomarkers were not (p‐values>0.35). In models including p‐tau231 and various biomarker combinations, p‐tau231 remained significant in each model (p‐values<0.02). In comparison to the base model with p‐tau231 only, models adding GFAP (AIC = 143.8, LRTest p = 0.008), GFAP and NfL (AIC = 145.5, LRTest p = 0.02), and GFAP and Aβ42/40 (AIC = 145.8, LRTest p = 0.03) improved prediction of amyloid positivity. The model including plasma p‐tau231 and GFAP was the best fitting model (C‐index = 0.89).ConclusionIn this sample of community‐dwelling older adults free of clinical dementia, we found plasma p‐tau231 and GFAP were associated with amyloid positivity. Models including both p‐tau231 and GFAP performed best, and adding additional plasma biomarkers to this combination did not produce a better fitting model. Taken together, these findings highlight the utility of plasma p‐tau231 for screening for amyloid status and suggest that including plasma GFAP will improve discriminative accuracy.

40 Educational Quality vs Years of Education is More Strongly Associated with Neuropsychological Test Performance

Objective:Education is known to impact neuropsychological test performance, and self-reported years of education is often used in stratifying normative data. However, this variable does not always reflect education quality, particularly among underrepresented populations, and may overestimate cognitive impairment in individuals with low education quality. This cross-sectional study evaluated relative contributions of years of education and reading level to several verbally mediated assessments to improve interpretation of neuropsychological performance.Participants and Methods:Data was obtained from the Vanderbilt Memory and Aging Project. Cognitively-unimpaired participants (n=175, 72±7 years, 59% male, 87% Non-Hispanic White, 16±2 years of education) completed a comprehensive neuropsychological protocol. Stepwise linear regressions were calculated using education and Wide Range Achievement Test (WRAT)-3 Reading subtest scores as predictors and letter fluency (FAS, CFL), category fluency (Vegetable and Animal Naming), the Boston Naming Test (BNT), and California Verbal Learning Test (CVLT)-II as outcomes to assess increase in variance explained by educational quality. Models covaried for age and sex. The False Discovery Rate (FDR) based on the Benjamini-Hochberg procedure (Benjamini & Hochberg, 1995) was used to correct for multiple comparisons.Results:The mean WRAT-3 score was 51±4 (range:37-57), indicating post-high school reading level. Education and WRAT-3 scores were moderately correlated (r=0.36, p<0.01). Both WRAT-3 and years of education independently predicted letter fluency (WRAT-3 p<0.001; education p<0.02), category fluency (WRAT-3 p<0.001; education p<0.05), and CVLT-II performance (WRAT-3 p-values<0.005; education p-values<0.02) in single predictor models. On BNT, WRAT-3 (p<0.001), but not education (p=0.06), predicted performance in single predictor models. In combined models with both WRAT-3 and education, WRAT-3 scores remained a significant predictor of FAS (WRAT-3 b=1.21, p<0.001; education b=0.006, p=0.99) and CFL performance (WRAT-3 b=1.02, p<0.001; education b=0.51, p=0.14). Both WRAT-3 (b=0.21, p=0.01) and years of education (b=0.35, p=0.03) predicted Animal Naming, while WRAT-3 (b=0.16,p=0.008), but not years of education (p=0.37), predicted Vegetable Naming. WRAT-3 was a significant predictor of BNT performance (b=0.21, p<0.001) but not years of education (p=0.65). WRAT-3 predicted CVLT-II learning (b=0.32, p=0.04), immediate recall (b=0.16, p=0.005), and delayed recall performances (b=0.15, p=0.005), while education did not (p-values>0.14). All significant results persisted after FDR correction. WRAT-3 scores explained an additional level of variance beyond the covariates and education alone for FAS (AR=18%), CFL (AR=13%), Animal Naming and Vegetable Naming (AR= 3%), BNT (AR=18%), and CVLT-II learning (AR=2%), immediate recall (AR=4%), and delayed recall (AR=3%).Conclusions:Reading level more strongly associated with performance on several verbally mediated neuropsychological measures than years of education. For all measures, the addition of reading level significantly increased the amount of variance explained by the model compared to covariates and education alone, which aligns with existing research. However, most of this past work looks at individuals with lower levels of educational quality. Because our cohort was highly educated and at the upper end of the reading spectrum, our results suggest that reading level is important to consider even for more highly educated individuals. Therefore, reading level is a critical variable to consider when interpreting verbally mediated neuropsychological measures for individuals across the educational spectrum.

A named General Practitioner (GP) is associated with an increase of hospital days in a single predictor analysis: a follow-up of 15 years

BackgroundContinuity of care constitutes the basis of primary health care services and is associated with decreased hospitalization. In Finland, accessibility to primary care and increased use of hospital services are recognized challenges for the health care system.ObjectivesThe aim of the study was to determine whether having a named GP is associated with hospital service use.MethodsThe data are part of the Health and Social Support study (HeSSup) based on a random Finnish working-age population sample. The cohort of the study comprised participants of postal surveys in 1998 (n = 25,898) who returned follow-up questionnaires both in 2003 and 2012 (n = 11,924). Background characteristics were inquired in the questionnaires, and hospitalization was derived from national registries (Hilmo-register).ResultsA named GP was reported both in 2003 and 2012 only by 34.3% of the participants. The association between hospital days and a named GP was linearly rising and statistically significant in a single predictor model. The strongest associations with hospital use were with health-related factors, and the association with a named GP was no longer significant in multinomial analysis.ConclusionA named GP is associated with an increased use of hospital days, but in a multinomial analysis the association disappeared. Health related factors showed the strongest association with hospital days. From the perspective of the on-going Finnish health and social services reform, continuity of care should be emphasized.

Evaluating the prediction performance of objective physical activity measures for incident Parkinson's disease in the UK Biobank.

Parkinson's disease (PD) is the fastest-growing neurological condition with over 10 million cases worldwide. While age and sex are known predictors of incident PD, there is a need to identify other predictors. This study compares the prediction performance of accelerometry-derived physical activity (PA) measures and traditional risk factors for incident PD in the UK Biobank. The study population consisted of 92,352 UK Biobank participants without PD at baseline (43.8% male, median age 63years with interquartile range 43-69). 245 participants were diagnosed with PD by April 1, 2021 (586,604 person-years of follow-up). The incident PD prediction performances of 10 traditional predictors and 8 objective PA measures were compared using single- and multi-variable Cox models. Prediction performance was assessed using a novel, stable statistic: the repeated cross-validated concordance (rcvC). Sensitivity analyses were conducted where PD cases diagnosed within the first six months, one year, and two years were deleted. Single-predictor Cox regression models indicated that all PA measures were statistically significant (p-values < 0.0001). The highest-performing individual predictors were total acceleration (TA) (rcvC = 0.813) among PA measures, and age (rcvC = 0.757) among traditional predictors. The two-step forward-selection process produced a model containing age, sex, and TA (rcvC = 0.851). Adding TA to the model increased the rcvC by 9.8% (p-value < 0.0001). Results were largely unchanged in sensitivity analyses. Objective PA summaries have better single-predictor model performance than known risk factors and increase the prediction performance substantially when added to models with age and sex.

A network analysis of depressive symptoms and metabolomics

Depression is associated with metabolic alterations including lipid dysregulation, whereby associations may vary across individual symptoms. Evaluating these associations using a network perspective yields a more complete insight than single outcome-single predictor models. We used data from the Netherlands Study of Depression and Anxiety (N = 2498) and leveraged networks capturing associations between 30 depressive symptoms (Inventory of Depressive Symptomatology) and 46 metabolites. Analyses involved 4 steps: creating a network with Mixed Graphical Models; calculating centrality measures; bootstrapping for stability testing; validating central, stable associations by extra covariate-adjustment; and validation using another data wave collected 6 years later. The network yielded 28 symptom-metabolite associations. There were 15 highly-central variables (8 symptoms, 7 metabolites), and 3 stable links involving the symptoms Low energy (fatigue), and Hypersomnia. Specifically, fatigue showed consistent associations with higher mean diameter for VLDL particles and lower estimated degree of (fatty acid) unsaturation. These remained present after adjustment for lifestyle and health-related factors and using another data wave. The somatic symptoms Fatigue and Hypersomnia and cholesterol and fatty acid measures showed central, stable, and consistent relationships in our network. The present analyses showed how metabolic alterations are more consistently linked to specific symptom profiles.

The Influence of Air Pollution on Non-Infectious Hospitalizations for Severe Acute Exacerbations of Chronic Obstructive Pulmonary Disease: A Time-Series from Serbia

The available data on the impact of air pollution on acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are inconsistent. We investigated the influence of air pollution on the number of severe AECOPD hospitalizations of non-infectious etiology in patients residing in Novi Sad, Serbia. In this time-series, we used a quasi-Poisson generalized linear model in conjunction with distributed lag non-linear models, after controlling for lag days, seasonal and long-term trends, and meteorological factors (air temperature and humidity), to estimate the relative risk (RR) of AECOPD hospitalization for each increase of 10 μg/m3 in the air pollutant concentration. A total of 552 AECOPD hospitalizations were registered during 2017–2022. With each 10 μg/m3 increase in the selected air pollutants’ concentration, the cumulative RR (lags0–7) in single-predictor models for AECOPD admission were 1.52 (95% CI 0.98–2.35) for PM10, 1.44 (95% CI 0.93–2.25) for PM2.5, 1.13 (95% CI 0.87–1.47) for SO2, and 0.99 (95% CI 0.69–1.42) for NO2. Similar results were found in multi-predictor models as well as in group analyses between smokers and non-smokers. In conclusion, no significant associations between exposure to air pollutants and the daily AECOPD admissions were found. There is an obvious need for additional research on the topic.

Abiotic and Biotic Factors from the Past as Predictors of Alien Bird Richness and Temporal Beta-Diversity

The challenge of predicting the distribution of alien species has long been a focus of invasion ecology. Herein, we assessed biotic and abiotic factors from the 1980s as potential predictors of alien bird species patterns 20 years later in the state of New York. To assess the ability of each factor to predict future alien species patterns, we analysed the influence of biotic (native taxonomic, functional and phylogenetic diversity, and human population density) and abiotic (climate and land use) factors from the 1980s on the observed alien species richness patterns in the 2000s and the temporal change in the composition of the alien communities between the 1980s and the 2000s using both single-predictor and multivariate models. Alien species richness from the 1980s was a reliable predictor of the alien species richness and temporal beta-diversity patterns in the 2000s. Among abiotic factors, maximum temperature and agricultural land-uses constituted sufficient predictors of future alien species richness and better predictors than the native biotic factors. The performance of single-predictor models was generally weaker in predicting temporal alien beta-diversity; however, past alien species richness and maximum temperature again outperformed the other factors. Predictions and management decisions should focus on warm and agricultural areas, as well as areas with an already high number of established alien species.

Re-modeling the solar diffuse fraction in Taiwan on basis of a typical-meteorological-year data

An objective approach to determine a dataset from which a correlation model of solar diffuse fraction is fitted and tested is proposed. A typical meteorological year (TMY) of global radiation data that is measured in Tainan, Taiwan over 10 years (2011–2020) is used to construct the training dataset and the remaining data (90% data base) is used as a test dataset. Two multiple-predictor and one single-predictor correlation models for the hourly diffuse fraction are developed by using more theoretically rigorous techniques for determining the breaking points for segmentation (excluding the modified Boland-Ridley-Lauret (BRL) model) and how many significant predictors required in each segmented interval for the regression model with piece-wise linear multiple-predictor correlation. The performance of each developed correlation model is superior to that of the existing same-type model using a part (one year or two years) of the same data base. The re-modeled piece-wise linear multiple-predictor correlation model has the best long-term performance of the three developed correlation models. The modified BRL model on basis of the TMY data is second. The re-modeled Liu-Jordan-type (single predictor) model allows real time prediction and has a simpler form than the other two models but prediction accuracy is inferior.

Combining Bayesian Calibration and Copula Models for Age Estimation.

Accurately estimating and predicting chronological age from some anthropometric characteristics of an individual without an identity document can be crucial in the context of a growing number of forced migrants. In the related literature, the prediction of chronological age mostly relies upon the use of a single predictor, which is usually represented by a dental/skeletal maturity index, or multiple independent ordinal predictor (stage of maturation). This paper is the first attempt to combine a robust method to predict chronological age, such as Bayesian calibration, and the use of multiple continuous indices as predictors. The combination of these two aspects becomes possible due to the implementation of a complex statistical tool as the copula. Comparing the forecasts from our copula-based method with predictions from an independent model and two single predictor models, we showed that the accuracy increased.

Gold and tail risks

In this study, we consider as a predictor of gold return predictability, an alternative measure of systematic risk using the tail risk obtained from the four variants (Adaptive, Symmetric absolute value, Asymmetric slope and Indirect GARCH) of the Conditional Autoregressive Value at Risk (CAViaR) of Engle and Manganelli (2004). We conduct distinct analyses for the gold-tail risk nexus for both 1% and 5% VaRs across the in-sample and out-of-sample forecasts. The results of the in-sample predictability indicate contrasting effects of own tail risk and oil tail risk (a proxy for global risk factor) with negative and positive effects, respectively on gold returns reinforcing the safe haven property of the gold market against global risk. Evidence of the out-of-sample predictability supports the inclusion of both own tail risk and oil tail risk over the benchmark model and single-predictor (own tail risk) model for improved out-of-sample forecasts of gold returns. The results leading to these conclusions are robust to alternative proxies for oil price and magnitudes of VaR.

Patients’ symptoms and strengths as predictors of long-term outcomes of CBT for generalized anxiety disorder – A three-level, multi-predictor analysis

Although cognitive behavioral therapy (CBT) is an effective treatment for generalized anxiety disorder (GAD), GAD often shows a chronic clinical course and common deterioration after treatment. Many trials have examined the efficacy of treatments in GAD, but little is known about intake predictors of long-term treatment outcomes. This study examined potential predictors of long-term treatment outcomes based on the individual’s symptom severity and strengths (behavioral, cognitive, interpersonal) at intake. Long-term outcomes were defined as worry at six-month follow-up (six-m FU) and worry decrease from intake and post-treatment to six-m FU. Data from 137 CBT outpatients with a GAD diagnosis from two randomized clinical trials were analyzed using three-level hierarchical linear modeling. Results revealed that worrying decreased up to the six-m FU. In single-predictor models, intake symptom severity and strength measures predicted worry at the six-m FU. In multi-predictor models, only behavioral strengths remained a significant predictor. Worry decrease from intake to the six-m FU was only predicted by behavioral strengths. These findings provide relevant information about intake predictors of long-term outcomes after CBT for GAD and underscore the potential relevance of assessing patients’ strengths for clinical practice.

Pandemic uncertainty and sectoral stock returns predictability in South Africa

PurposeIn this paper, the author examines the role of uncertainty due to pandemic on the predictability of sectoral stock returns in South Africa. This is motivated by the ongoing global pandemic, COVID-19, in predicting sector stock returns.Design/methodology/approachThe study considers estimation of dynamic panel data with dynamic common correlated effects estimator and two pair-wise forecast measures, namely Campbell and Thompson (2008) and Clark and West (2007) tests in dealing with the nested predictive models.FindingsThe results show that pandemic uncertainty has a negative and statistically significant effect on the different sector returns, implying that sector stock returns decline as the pandemic outbreak becomes more pronounced. While the single predictor model consistently outperforms the historical average model both for in-sample and out-of-sample, controlling for other macroeconomic variables effect improves the forecast accuracy of infectious diseases uncertainty. These results are consistently robust to both the in-sample and out-of-sample forecast periods, outliers and heterogeneity. These results have implications for portfolio diversification strategies, which we set aside for future research.Originality/valueThe empirical literature is satiated with studies on how news can predict economic and financial variables, however, the role of uncertainty due to infectious diseases in the stock return predictability especially at the sectoral level is less understudied, this is the main contribution of the study.

The Construction of a Predictive Composite Index for Decision-Making of CSF Diversion Surgery in Pediatric Patients following Prenatal Myelomeningocele Repair.

There is a wide range of clinical and radiographic factors affecting individual surgeons' ultimate decision for CSF diversion for pediatric patients following prenatal myelomeningocele repair. Our aim was to construct a composite index (CSF diversion surgery index) that integrates conventional clinical measures and neuroimaging biomarkers to predict CSF diversion surgery in these pediatric patients. This was a secondary retrospective analysis of data from 33 patients with prenatal myelomeningocele repair (including 14 who ultimately required CSF diversion surgery). Potential independent variables, including the Management of Myelomeningocele Study Index (a dichotomized variable based on the shunt-placement criteria from the Management of Myelomeningocele Study), postnatal DTI measures (fractional anisotropy and mean diffusivity in the genu of the corpus callosum and the posterior limb of internal capsule), fronto-occipital horn ratio at the time of DTI, gestational ages, and sex, were evaluated using stepwise logistic regression analysis to identify the most important predictors. The CSF diversion surgery index model showed that the Management of Myelomeningocele Study Index and fractional anisotropy in the genu of the corpus callosum were significant predictors (P < .05) of CSF diversion surgery. The predictive value of the CSF diversion surgery index was also affected by fractional anisotropy in the posterior limb of the internal capsule and sex with marginal effect (.05<P < .10), but not by the fronto-occipital horn ratio (P > .10). The overall CSF diversion surgery index model fit the data well with statistical significance (eg, likelihood ratio: P < .001), with the performance (sensitivity = 78.6%; specificity = 86.5%, overall accuracy = 84.8%) superior to all individual indices in sensitivity and overall accuracy, and most of the individual indices in specificity. The CSF diversion surgery index model outperformed all single predictor models and, with additional validation, may potentially be developed and incorporated into a sensitive and robust clinical tool to assist clinicians in hydrocephalus management.