Split-half Cross-validation Research Articles

Predicting Soluble Solids Concentration of ‘Geneva 3’ Kiwiberries Using Near Infrared Spectroscopy

Near infrared (NIR) spectroscopy can be applied to nondestructively assess soluble solids concentration (SSC) of ripening, physiologically mature ‘Geneva 3’ kiwiberries (Actinidia arguta). Spectrographic signatures were captured using a handheld NIR produce quality meter to build predictive models of internal fruit quality for ‘Geneva 3’ kiwiberries that had been held under cold storage (CS) conditions (0 to 1 °C, >90% relative humidity) as well as those not subjected to CS. The CS model, constructed using scans of 133 berries following 4 to 6 weeks in CS, predicts SSC using NIR wavelengths in the range of 729 to 975 nm. A total of 507 berries fresh from the vine were used to construct a predictive model for SSC of non-CS fruit using the same wavelength range. In each case, model predictive performance was investigated using split-half cross-validation, resulting in mean absolute error (MAE) values of 1.2% and 0.8% SSC for the CS and non-CS model, respectively. Each full model was then used to predict SSC of kiwiberries subjected to the alternative CS condition. The non-CS model maintained a low MAE (1.6% SSC) when applied to CS fruit, but the MAE of the CS model applied to non-CS fruit rose considerably (4.5% SSC). The performance of a combined model was tested against both CS and non-CS models, and a benefit to using tailored, CS-specific models was found, particularly in light of cross-seasonal results. As it has proven in many crops, NIR spectroscopy appears to be a promising tool for nondestructively assessing SSC in ‘Geneva 3’ kiwiberry fruit, with accuracy being enhanced by training models specific to postharvest regimes and/or defined ranges of SSC.

Mapping of Family Reported Outcome Measure (FROM-16) scores to EQ-5D: algorithm to calculate utility values

ObjectiveAlthough decision scientists and health economists encourage inclusion of family member/informal carer utility in health economic evaluation, there is a lack of suitable utility measures comparable to patient utility measures such those based on the EQ-5D. This study aims to predict EQ-5D-3L utility values from Family Reported Outcome Measure (FROM-16) scores, to allow the use of FROM-16 data in health economic evaluation when EQ-5D data is not available.MethodsData from 4228 family members/partners of patients recruited to an online cross-sectional study through 58 UK-based patient support groups, three research support platforms and Welsh social services departments were randomly divided five times into two groups, to derive and test a mapping model. Split-half cross-validation was employed, resulting in a total of ten multinomial logistic regression models. The Monte Carlo simulation procedure was used to generate predicted EQ-5D-3L responses, and utility scores were calculated and compared against observed values. Mean error and mean absolute error were calculated for all ten validation models. The final model algorithm was derived using the entire sample.ResultsThe model was highly predictive, and its repeated fitting using multinomial logistic regression demonstrated a stable model. The mean differences between predicted and observed health utility estimates ranged from 0.005 to 0.029 across the ten modelling exercises, with an average overall difference of 0.015 (a 2.2% overestimate, not of clinical importance).ConclusionsThe algorithm developed will enable researchers and decision scientists to calculate EQ-5D health utility estimates from FROM-16 scores, thus allowing the inclusion of the family impact of disease in health economic evaluation of medical interventions when EQ-5D data is not available.

Scale development to evaluate differences between concern about falling and fear of falling: the concern and fear of falling evaluation.

Individuals with multiple sclerosis (MS) experience fear of falling (FOF), which is associated with negative health and quality-of-life consequences. Prior research has used FOF and concern about falling (CAF) interchangeably, but persons with MS report that CAF and FOF represent separate constructs that lie on a continuum. Unfortunately, no scale exists to understand the differences between CAF and FOF. Therefore, we developed a novel questionnaire, the Concern and Fear of Falling Evaluation (CAFFE), in which respondents rank their CAF and FOF on a continuum across various activities. This study aims to describe the scale development process and examine its psychometric properties. In a single online survey, MS participants responded to demographic questionnaires, indicated whether they experience CAF and FOF, and completed the CAFFE. Psychometric evaluation of the CAFFE involved internal consistency, split-half cross validation, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). Out of 1,025 respondents, 64.6% reported CAF and 47.2% reported FOF. The EFA yielded a two-factor solution encompassing activities in open (factor 1) and closed environments (factor 2). The CFA replicated this two-factor solution and the CAFFE demonstrated excellent internal consistency (α = 0.98). The 27-item CAFFE is a highly reliable and valid measure capturing the tipping point at which point CAF moves to FOF. Future research should seek to define the tipping point from the MS community, as CAF may be an adaptive mechanism, whereas FOF may be a maladaptive behavior.

Linking Individual Differences in Personalized Functional Network Topography to Psychopathology in Youth

BackgroundThe spatial layout of large-scale functional brain networks differs between individuals and is particularly variable in the association cortex, implicated in a broad range of psychiatric disorders. However, it remains unknown whether this variation in functional topography is related to major dimensions of psychopathology in youth. MethodsThe authors studied 790 youths ages 8 to 23 years who had 27 minutes of high-quality functional magnetic resonance imaging data as part of the Philadelphia Neurodevelopmental Cohort. Four correlated dimensions were estimated using a confirmatory correlated traits factor analysis on 112 item-level clinical symptoms, and one overall psychopathology factor with 4 orthogonal dimensions were extracted using a confirmatory factor analysis. Spatially regularized nonnegative matrix factorization was used to identify 17 individual-specific functional networks for each participant. Partial least square regression with split-half cross-validation was conducted to evaluate to what extent the topography of personalized functional networks encodes major dimensions of psychopathology. ResultsPersonalized functional network topography significantly predicted unseen individuals’ major dimensions of psychopathology, including fear, psychosis, externalizing, and anxious-misery. Reduced representation of association networks was among the most important features for the prediction of all 4 dimensions. Further analysis revealed that personalized functional network topography predicted overall psychopathology (r = 0.16, permutation testing p < .001), which drove prediction of the 4 correlated dimensions. ConclusionsThese results suggest that individual differences in functional network topography in association networks is related to overall psychopathology in youth. Such results underscore the importance of considering functional neuroanatomy for personalized diagnostics and therapeutics in psychiatry.

Cross-validation and permutations in MVPA: Validity of permutation strategies and power of cross-validation schemes

Multi-Voxel Pattern Analysis (MVPA) is a well established tool to disclose weak, distributed effects in brain activity patterns. The generalization ability is assessed by testing the learning model on new, unseen data. However, when limited data is available, the decoding success is estimated using cross-validation. There is general consensus on assessing statistical significance of cross-validated accuracy with non-parametric permutation tests. In this work we focus on the false positive control of different permutation strategies and on the statistical power of different cross-validation schemes.With simulations, we show that estimating the entire cross-validation error on each permuted dataset is the only statistically valid permutation strategy. Furthermore, using both simulations and real data from the HCP WU-Minn 3T fMRI dataset, we show that, among the different cross-validation schemes, a repeated split-half cross-validation is the most powerful, despite achieving slightly lower classification accuracy, when compared to other schemes. Our findings provide additional insights into the optimization of the experimental design for MVPA, highlighting the benefits of having many short runs.

Psychometric Properties of the Problem-Oriented Patient Experience-Primary Care (POPE-PC) Survey.

Measuring the experiences of patients regarding delivery and receipt of person-oriented primary care is of increasing policy and research interest and is a core component of the Institute for Healthcare Improvement's Quadruple Aim. To describe the Problem-Oriented Patient Experience-Primary Care (POPE-PC) survey, a novel instrument designed to measure patients' experiences of primary care, and to assess the instrument's psychometric properties. Psychometric testing was performed using data from a Canadian urgent primary care center, derived from March 2019 to September 2019. Patients automatically received the 9-question survey by email after leaving the clinic. Exploratory factor analysis (EFA) on all questions and the entire dataset was performed using parallel analysis and scree plot for factor extraction. Internal consistency was assessed by calculating Cronbach α. A split-half cross-validation of the ensuing factor structure was conducted. A correlation analysis helped explore associations between the survey's questions. Results from the initial EFA indicate that the POPE-PC has a conceptually sound 2-factor structure, with good internal consistency. A split-half validation yielded the same findings, reaffirming that the 2-factor model has good psychometric properties. The correlation analysis indicated that the concept of respect is strongly associated with clinical functions related to problem recognition. Problem recognition, despite being the cornerstone of person-oriented primary care, remains largely overlooked in health services research. The POPE-PC's validity and problem orientation render it potentially useful in rigorously assessing patient experiences of problem-oriented primary care. The survey's conceptual underpinning and psychometric properties, coupled with its simple and parsimonious design, enable application in primary care settings to provide person-oriented care.

Autism Identity and the "Lost Generation": Structural Validation of the Autism Spectrum Identity Scale (ASIS) and Comparison of Diagnosed and Self-Diagnosed Adults on the Autism Spectrum.

A population segment of autistic adults are under-identified due, in part, to historic changes in criteria for diagnosing autism and diagnostic biases related to gender, socioeconomic status, and other individual characteristics such as intellectual functioning. Some of these individuals, described as the "lost generation", may choose to self-diagnose. Although little is known about this population, it is possible that they share similar self-conceptualizations or internalized stigma as their diagnosed counterparts. This study reports on the structural validity of the Autism Spectrum Identity Scale (ASIS) with individuals diagnosed and self-diagnosed with autism and compares the demographic characteristics, stigma, self-concept, and quality of life of these two groups. Over 1000 adults diagnosed (n = 893) or self-diagnosed (n = 245) with autism were recruited through organizations serving the autism community to participate in a nationally distributed online survey that included demographic questions and measures for stigma, self-concept, quality of life, and wellbeing. The diagnosed dataset was randomly split with exploratory factor analysis performed on a training dataset. Split-half cross-validation was used to predict the factor structure of the holdout dataset. Then, the full diagnosed dataset structure was used to determine the generalizability of the factor structure to the self-diagnosed dataset. The diagnosed and self-diagnosed were also compared for differences in gender, age, employment status, diagnostic term preference, and factors of self-concept (autism identity and self-esteem), stigma, and quality of life. Factor analysis of diagnosed participants yielded a four-factor structure, consistent with previous research, with strong split-sample cross-validation and good internal consistency. Factor predictions of the self-diagnosed dataset from the diagnosed dataset ranged from .97 - 1.00 with similar internal consistency. Self-diagnosed participants were more likely to be older, women, or employed and less likely to be students or prefer the term "autism" than those with an autism diagnosis. The groups were remarkably similar in reported stigma, self-esteem, quality of life and in ASIS factors; both groups reported lower quality of life than the general population. The ASIS demonstrated the same internal structure with both the diagnosed and self-diagnosed. The profile of self-diagnosed participants matches the profile hypothesized for the "lost generation" and others at risk of being under-identified for autism. Both populations appear to be similarly struggling with employment, stigma, and quality of life. Future research should examine whether self-diagnosed individuals meet criteria for autism or could benefit from interventions, programs, or services serving autism communities.

Integrated Primary Care Readiness and Behaviors Scale: Development and validation in behavioral health professionals.

Although integrated primary care (IPC) is growing, several barriers remain. Better understanding of behavioral health professionals' (BHPs') readiness for and engagement in IPC behaviors could improve IPC research and training. This study developed measures of IPC behaviors and stage of change. The sample included 319 licensed, practicing BHPs with a range of interests and experience with IPC. Sequential measurement development procedures, with split-half cross-validation were conducted. Exploratory principal components analyses (N = 152) and confirmatory factor analyses (N = 167) yielded a 12-item scale with 2 factors: consultation/practice management (CPM) and intervention/knowledge (IK). A higher-order Integrated Primary Care Behavior Scale (IPCBS) model showed good fit to the data, and excellent internal consistencies. The multivariate analysis of variance (MANOVA) on the IPCBS demonstrated significant large-sized differences across stage and behavior groups. The IPCBS demonstrated good psychometric properties and external validation, advancing research, education, and training for IPC practice. (PsycINFO Database Record

Identification of Heterogeneous Cognitive Subgroups in Community-Dwelling Older Adults: A Latent Class Analysis of the Einstein Aging Study.

The aim of this study was to identify natural subgroups of older adults based on cognitive performance, and to establish each subgroup's characteristics based on demographic factors, physical function, psychosocial well-being, and comorbidity. We applied latent class (LC) modeling to identify subgroups in baseline assessments of 1345 Einstein Aging Study (EAS) participants free of dementia. The EAS is a community-dwelling cohort study of 70+ year-old adults living in the Bronx, NY. We used 10 neurocognitive tests and 3 covariates (age, sex, education) to identify latent subgroups. We used goodness-of-fit statistics to identify the optimal class solution and assess model adequacy. We also validated our model using two-fold split-half cross-validation. The sample had a mean age of 78.0 (SD=5.4) and a mean of 13.6 years of education (SD=3.5). A 9-class solution based on cognitive performance at baseline was the best-fitting model. We characterized the 9 identified classes as (i) disadvantaged, (ii) poor language, (iii) poor episodic memory and fluency, (iv) poor processing speed and executive function, (v) low average, (vi) high average, (vii) average, (viii) poor executive and poor working memory, (ix) elite. The cross validation indicated stable class assignment with the exception of the average and high average classes. LC modeling in a community sample of older adults revealed 9 cognitive subgroups. Assignment of subgroups was reliable and associated with external validators. Future work will test the predictive validity of these groups for outcomes such as Alzheimer's disease, vascular dementia and death, as well as markers of biological pathways that contribute to cognitive decline. (JINS, 2018, 24, 511-523).

Brain Networks Implicated in Seasonal Affective Disorder: A Neuroimaging PET Study of the Serotonin Transporter.

Background: Seasonal Affective Disorder (SAD) is a subtype of Major Depressive Disorder characterized by seasonally occurring depression that often presents with atypical vegetative symptoms such as hypersomnia and carbohydrate craving. It has recently been shown that unlike healthy people, patients with SAD fail to globally downregulate their cerebral serotonin transporter (5-HTT) in winter, and that this effect seemed to be particularly pronounced in female S-carriers of the 5-HTTLPR genotype. The purpose of this study was to identify a 5-HTT brain network that accounts for the adaption to the environmental stressor of winter in females with the short 5-HTTLPR genotype, a specific subgroup previously reported to be at increased risk for developing SAD.Methods: Nineteen females, either S' carriers (LG- and S-carriers) without SAD (N = 13, mean age 23.6 ± 3.2 year, range 19–28) or S' carriers with SAD (N = 6, mean age 23.7 ± 2.4, range 21–26) were PET-scanned with [11C]DASB during both summer and winter seasons (asymptomatic and symptomatic phase, 38 scans in total) in randomized order, defined as a 12-week interval centered on summer or winter solstice. We used a multivariate Partial Least Squares (PLS) approach with NPAIRS split-half cross-validation, to identify and map a whole-brain pattern of 5-HTT levels that distinguished the brains of females without SAD from females suffering from SAD.Results: We identified a pattern of 5-HTT levels, distinguishing females with SAD from those without SAD; it included the right superior frontal gyrus, brainstem, globus pallidus (bilaterally) and the left hippocampus. Across seasons, female S' carriers without SAD showed nominally higher 5-HTT levels in these regions compared to female S' carriers with SAD, but the group difference was only significant in the winter. Female S' carriers with SAD, in turn, displayed robustly increased 5-HTT levels in the ventral striatum (bilaterally), right orbitofrontal cortex, middle frontal gyrus (bilaterally), extending to the left supramarginal gyrus, left precentral gyrus and left postcentral gyrus during winter compared to female S' carriers without SAD.Limitations: The study is preliminary and limited by small sample size in the SAD group (N = 6).Conclusions: These findings provide novel exploratory evidence for a wintertime state-dependent difference in 5-HTT levels that may leave SAD females with the short 5-HTTLPR genotype more vulnerable to persistent stressors like winter. The affected brain regions comprise a distributed set of areas responsive to emotion, voluntary, and planned movement, executive function, and memory. The preliminary findings provide additional insight into the neurobiological components through which the anatomical distribution of serotonergic discrepancies between individuals genetically predisposed to SAD, but with different phenotypic presentations during the environmental stressor of winter, may constitute a potential biomarker for resilience against developing SAD.

Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression

PurposeThe Dermatology Life Quality Index (DLQI) and the European Quality of Life-5 Dimension (EQ-5D) are separate measures that may be used to gather health-related quality of life (HRQoL) information from patients. The EQ-5D is a generic measure from which health utility estimates can be derived, whereas the DLQI is a specialty-specific measure to assess HRQoL. To reduce the burden of multiple measures being administered and to enable a more disease-specific calculation of health utility estimates, we explored an established mathematical technique known as ordinal logistic regression (OLR) to develop an appropriate model to map DLQI data to EQ-5D-based health utility estimates.MethodsRetrospective data from 4010 patients were randomly divided five times into two groups for the derivation and testing of the mapping model. Split-half cross-validation was utilized resulting in a total of ten ordinal logistic regression models for each of the five EQ-5D dimensions against age, sex, and all ten items of the DLQI. Using Monte Carlo simulation, predicted health utility estimates were derived and compared against those observed. This method was repeated for both OLR and a previously tested mapping methodology based on linear regression.ResultsThe model was shown to be highly predictive and its repeated fitting demonstrated a stable model using OLR as well as linear regression. The mean differences between OLR-predicted health utility estimates and observed health utility estimates ranged from 0.0024 to 0.0239 across the ten modeling exercises, with an average overall difference of 0.0120 (a 1.6% underestimate, not of clinical importance).ConclusionsThis modeling framework developed in this study will enable researchers to calculate EQ-5D health utility estimates from a specialty-specific study population, reducing patient and economic burden.

Validity and reliability of HOP-Up: a questionnaire to evaluate physical activity environments in homes with preschool-aged children.

BackgroundEarly identification of physical activity (PA) opportunities in the home and neighborhood environment may help obesity prevention efforts in households with young children. This cross-sectional study’s purpose was to develop a brief, easy-to-use, self-report inventory called Home Opportunities for Physical activity check-Up (HOP-Up), to evaluate the availability and accessibility of PA space and equipment in and near homes with preschool children, and establish its validity and reliability.MethodsThe HOP-Up was field tested by two trained researchers and parents of preschool-aged children (n = 50; 71 % white). To establish criterion validity, researchers were the ‘gold standard’ and visited participants’ homes to assess their PA environments using the HOP-Up, while participants separately completed their HOP-Up. Two weeks later, parents completed the HOP-Up online for test-retest reliability. After minor survey refinements, exploratory and confirmatory factor analysis using a split-half cross validation procedure was conducted in a larger sample of participants (n = 655, 60 % white) who completed the HOP-Up online to examine its factor structure. To establish convergent validity, correlations were conducted to compare HOP-Up scales from the factor solution generated with PA behavior and cognitions, and reported screen time.ResultsIntra-class correlations (ICCs) examining HOP-Up item agreement between researcher and parents revealed slight to substantial agreement (range 0.22 to 0.81) for all items. ICCs for all HOP-Up items ranged from fair to substantial agreement between parent responses at both time points (range 0.42 to 0.95). Exploratory factor analysis revealed a five factor solution (18 items), supported eigen values, scree plots, review for contextual sense, and confirmatory factor analysis. Additionally, there were significant (p < 0.05) positive correlations among nearly all five HOP-Up scales with parent and child physical activity levels (range 0.08 to 0.35), and values parents placed on PA for self and child (range 0.16 to 0.35), and negative correlations of Neighborhood Space & Supports for PA scale with parent and child reported screen time (r = −0.11, r = −0.13, respectively).ConclusionsFindings support the psychometric properties of this brief, easy-to-use, HOP-Up questionnaire, which may help parents, prevention researchers, residential planners, and practitioners increase their understanding of how the home environment—inside, outside, and the neighborhood— impacts preschool children’s physical activity levels.

Evaluating Measures of Fidelity for Substance Abuse Group Treatment With Incarcerated Adolescents

The evaluation of treatment fidelity has become increasingly important as the demand for evidence-based practice grows. The purpose of the present study is to describe the psychometric properties of two measures of treatment fidelity that can be used by therapists and supervisors - one for group-based cognitive–behavioral therapy (CBT) and one for combined Substance Education and Twelve-Step Introduction (SET) for adolescent substance use. At the end of group sessions (CBT n=307; SET n=279), therapists and supervisors completed an evaluation measure assessing adherence to certain core components of the intervention. The supervisor version of the fidelity measure also included items for rating the level of competency the therapist demonstrated when providing each component of the intervention. Results from split-half cross-validation analyses provide strong support for an 11-item, three-factor CBT fidelity measure. Somewhat less consistent but adequate support for a nine-item, two-factor SET fidelity measure was found. Internal consistencies ranged from acceptable to good for both the CBT and SET adherence scales and from acceptable to good for the CBT and SET competency scales, with the exception of the CBT practices competency scale. Preliminary validation of the measures suggests that both measures have adequate to strong factor structure, reliability, and concurrent and discriminant validity. The results of this study have implications for research and clinical settings, including the supervision process.

Low Back Pain Patient Subgroups in Primary Care

In industrialized countries, low back pain (LBP) is one of the leading causes for prolonged sick leave, early retirement, and high health care costs. Providing the same treatments to all patients is neither effective nor feasible, and may impede patients' recovery. Recent studies have outlined the need for subgroup-specific treatment allocation. This is a cross-sectional study that used baseline data from consecutively recruited patients participating in a guideline implementation trial regarding LBP in primary care. Classification variables were employment status, age, pain intensity, functional capacity (HFAQ), depression (CES-D), belief that activity causes pain (FABQ subscale), 2 scales of the SF-36 (general health, vitality), and days in pain per year. We performed k-means cluster analyses and split-half cross-validation. Subsequently, we investigated whether the resulting groups incurred different direct and indirect costs during a 6-month period before the index consultation. A 4-cluster solution showed good statistical quality criteria, even after split-half cross-validation. "Elderly patients adapted to pain" (cluster 1) and "younger patients with acute pain" (cluster 4) accounted for 55% of all patients. Cluster validation showed the lowest direct and indirect costs in these groups. About 72% of total costs per patient referred to clusters 2 and 3 ("patients with chronic severe pain with comorbid depression" and "younger patients with subacute pain and emotional distress"). Our study adds substantially to the knowledge of LBP-related case-mix in primary care. Information on differential health care needs may be inferred from our study, enabling decision makers to allocate resources more appropriately and to reduce costs.

Comparison of the glomerular filtration rate in children by the new revised Schwartz formula and a new generalized formula

The most widely used formula for estimating glomerular filtration rate (eGFR) in children is the Schwartz formula. It was revised in 2009 using iohexol clearances with measured GFR (mGFR) ranging between 15 and 75 ml/min × 1.73 m(2). Here we assessed the accuracy of the Schwartz formula using the inulin clearance (iGFR) method to evaluate its accuracy for children with less renal impairment comparing 551 iGFRs of 392 children with their Schwartz eGFRs. Serum creatinine was measured using the compensated Jaffe method. In order to find the best relationship between iGFR and eGFR, a linear quadratic regression model was fitted and a more accurate formula was derived. This quadratic formula was: 0.68 × (Height (cm)/serum creatinine (mg/dl))-0.0008 × (height (cm)/serum creatinine (mg/dl))(2)+0.48 × age (years)-(21.53 in males or 25.68 in females). This formula was validated using a split-half cross-validation technique and also externally validated with a new cohort of 127 children. Results show that the Schwartz formula is accurate until a height (Ht)/serum creatinine value of 251, corresponding to an iGFR of 103 ml/min × 1.73 m(2), but significantly unreliable for higher values. For an accuracy of 20 percent, the quadratic formula was significantly better than the Schwartz formula for all patients and for patients with a Ht/serum creatinine of 251 or greater. Thus, the new quadratic formula could replace the revised Schwartz formula, which is accurate for children with moderate renal failure but not for those with less renal impairment or hyperfiltration.

Adjusting for global effects in voxel-based morphometry: Gray matter decline in normal aging

Results from studies that have examined age-related changes in gray matter based on structural MRI scans have not always been consistent. Reasons for this variability likely include small or unevenly-distributed samples, different methods for tissue class segmentation and spatial normalization, and the use of different statistical models. Particularly relevant to the latter is the method of adjusting for global (total) gray matter when making inferences about regionally-specific changes. In the current study, we use voxel-based morphometry (VBM) to explore the impact of these methodological choices in assessing age-related changes in gray matter volume in a sample of 420 adults evenly distributed between the ages of 18–77 years. At a broad level, we replicate previous findings, showing age-related gray matter decline in nearly all parts of the brain, with particularly rapid decline in inferior regions of frontal cortex (e.g., insula and left inferior frontal gyrus) and the central sulcus. Segmentation was improved by increasing the number of tissue classes and using less age-biased templates, and registration was improved by using a diffeomorphic flow-based algorithm (DARTEL) rather than a “constrained warp” approach. Importantly, different approaches to adjusting for global effects – not adjusting, Local Covariation, Global Scaling, and Local Scaling – significantly affected regionally-specific estimates of age-related decline, as demonstrated by ranking age effects across anatomical ROIs. Split-half cross-validation showed that, on average, Local Covariation explained a greater proportion of age-related variance across these ROIs than did Global Scaling. Nonetheless, the appropriate choice for global adjustment depends on one's assumptions and specific research questions. More generally, these results emphasize the importance of being explicit about the assumptions underlying key methodological choices made in VBM analyses and the inferences that follow.

Measuring Hospital Safety Culture: Testing the Hsopsc Scale

As part of an organization's safety management strategy, it is important to assess the level of safety climate with a valid instrument. The aim of this study is to investigate the psychometric properties of the Hospital Survey on Patient Safety Culture (HSOPSC) based on a sample of clinical staff from Scottish acute hospitals and to determine the suitability for Scottish healthcare setting. The data were collected from 1966 clinical staff (estimated 22% response rate) from one acute hospital from each of seven Scottish Health Boards. In order to test the psychometric properties of the questionnaire, a split-half cross-validation technique was used. The data were randomly split into two, and Exploratory (EFA) and Confirmatory factor analyses (CFA) were conducted on the calibration and validation data sets to investigate and check the original US model fit from a Scottish sample. EFA results showed a 10 factor optimal measurement model. The CFA were then performed to compare the model fit of two alternative models (10 factor alternative model vs. 12 factor original model). It was demonstrated that both factor structures performed equally well in a Scottish sample. Furthermore, reliability analyses of each component yielded satisfactory results. Therefore no modifications are required to the original 12 factor model which is suggested for use since it would allow researchers the possibility of cross-national comparisons.

Assessment of the Pros and Cons of Stress Management among Adolescents: Development and Validation of a Decisional Balance Measure

This article reports on the development of a decisional balance measure based on the transtheoretical model (TTM) for stress management among adolescents. Measurement development was conducted with participants of a pilot survey. Surveys were completed in health and physical education classes. Ninth and tenth graders (N=317) completed an assessment of TTM constructs. The focus of this paper is the development of a decisional balance measure. A split-half cross-validation procedure was implemented. A brief reliable eight-item instrument was developed measuring the pros and cons of practicing stress management. A theoretically consistent pattern of pros and cons across the stages of change was obtained and indicated construct validity. Girls were more likely to practice and acknowledge benefits of stress management. This instrument will aid future work on promoting stress management practices among adolescents, particularly when using the TTM to develop interventions.

Quantitative electroencephalographic profiles for children with autistic spectrum disorder.

The present study examined quantitative electroencephalographic (QEEG) profile for children with autistic spectrum disorder (ASD). Five-minute QEEG data were obtained from 90 normal controls (NCs) and 66 children with ASD. Spectrum analyses revealed that ASD children showed significantly less relative alpha and more relative delta than NC. Specifically, 26% of ASD children and 2% of NCs showed 1.5 SDs of relative alpha below the normative mean. Children with this QEEG profile had 17 times the risk of having ASD than those without such a profile. Sensitivity and specificity of relative alpha were 91% and 73%, respectively. Split-half cross-validation yielded a sensitivity of 76%.

Risk Equation Determining Unsuccessful Cannulation Events and Failure to Maturation in Arteriovenous Fistulas (REDUCE FTM I)

Fistulas are the preferred permanent hemodialysis vascular access but a significant obstacle to increasing their prevalence is the fistula's high "failure to mature" (FTM) rate. This study aimed to (1) identify preoperative clinical characteristics that are predictive of fistula FTM and (2) use these predictive factors to develop and validate a scoring system to stratify the patient's risk for FTM. From a derivation set of 422 patients who had a first fistula created, a prediction rule was created using multivariate stepwise logistic regression. The model was internally validated using split-half cross-validation and bootstrapping techniques. A simple scoring system was derived and externally validated on 445 different, prospective patients who received a new fistula at five large North American dialysis centers. The clinical predictors that were associated with FTM were aged > or =65 yr (odds ratio [OR] 2.23; 95% confidence interval [CI] 1.25 to 3.96), peripheral vascular disease (OR 2.97; 95% CI 1.34 to 6.57), coronary artery disease (OR 2.83; 95% CI 1.60 to 5.00), and white race (OR 0.43; 95% CI 0.24 to 0.75). The resulting scoring system, which was externally validated in 445 patients, had four risk categories for fistula FTM: low (24%), moderate (34%), high (50%), and very high (69%; trend P < 0.0001). A preoperative, clinical prediction rule to determine fistulas that are likely to fail maturation was created and rigorously validated. It was found to be simple and easily reproducible and applied to predictive risk categories. These categories predicted risk of FTM to be 24, 34, 50, and 69% and are dependent on age, coronary artery disease, peripheral vascular disease, and race. The clinical utility of these risk categories in increasing rates of permanent accesses requires further clinical evaluation.