Summary Metrics Research Articles

Principal components of thermal regimes in mountain river networks

Abstract. Description of thermal regimes in flowing waters is key to understanding physical processes, enhancing predictive abilities, and improving bioassessments. Spatially and temporally sparse data sets, especially in logistically challenging mountain environments, have limited studies on thermal regimes, but inexpensive sensors coupled with crowd-sourced data collection efforts provide efficient means of developing large data sets for robust analyses. Here, thermal regimes are assessed using annual monitoring records compiled from several natural resource agencies in the northwestern United States that spanned a 5-year period (2011–2015) at 226 sites across several contiguous montane river networks. Regimes were summarized with 28 metrics and principal component analysis (PCA) was used to determine those metrics which best explained thermal variation on a reduced set of orthogonal axes. Four principal components (PC) accounted for 93.4 % of the variation in the temperature metrics, with the first PC (49 % of variance) associated with metrics that represented magnitude and variability and the second PC (29 % of variance) associated with metrics representing the length and intensity of the winter season. Another variant of PCA, T-mode analysis, was applied to daily temperature values and revealed two distinct phases of spatial variability – a homogeneous phase during winter when daily temperatures at all sites were <3 ∘C and a heterogeneous phase throughout the year's remainder when variation among sites was more pronounced. Phase transitions occurred in March and November, and coincided with the abatement and onset of subzero air temperatures across the study area. S-mode PCA was conducted on the same matrix of daily temperature values after transposition and indicated that two PCs accounted for 98 % of the temporal variation among sites. The first S-mode PC was responsible for 96.7 % of that variance and correlated with air temperature variation (r=0.92), whereas the second PC accounted for 1.3 % of residual variance and was correlated with discharge (r=0.84). Thermal regimes in these mountain river networks were relatively simple and responded coherently to external forcing factors, so sparse monitoring arrays and small sets of summary metrics may be adequate for their description. PCA provided a computationally efficient means of extracting key information elements from the temperature data set used here and could be applied broadly to facilitate comparisons among more diverse stream types and develop classification schemes for thermal regimes.

Delineating cerebellar mechanisms in DYT11 myoclonus-dystonia.

Recent research has highlighted the role of the cerebellum in the pathophysiology of myoclonus-dystonia syndrome as a result of mutations in the ɛ-sarcoglycan gene (DYT11). Specifically, a cerebellar-dependent saccadic adaptation task is dramatically impaired in this patient group. The objective of this study was to investigate whether saccadic deficits coexist with impairments of limb adaptation to provide a potential mechanism linking cerebellar dysfunction to the movement disorder within symptomatic body regions. Limb adaptation to visuomotor (visual feedback rotated by 30°) and forcefield (force applied by robot to deviate arm) perturbations were examined in 5 patients with DYT11 and 10 aged-matched controls. Patients with DYT11 successfully adapted to both types of perturbation. Modelled and averaged summary metrics that captured adaptation behaviors were equivalent to the control group across conditions. DYT11 is not characterized by a uniform deficit in adaptation. The previously observed large deficit in saccadic adaption is not reflected in an equivalent deficit in limb adaptation in symptomatic body regions. We suggest potential mechanisms at the root of this discordance and identify key research questions that need future study. © 2018 International Parkinson and Movement Disorder Society.

Large‐scale environmental flow results in mixed outcomes with short‐term benefits for a semi‐arid floodplain plant community

Abstract Demonstrating and predicting the effectiveness of environmental watering to maintain ecosystem health is becoming increasingly important, notably in semi‐arid floodplain ecosystems. Modelling plant floodplain vegetation responses to inform environmental watering outcomes is also challenging. We evaluated the effects of a large‐scale environmental flow event on a semi‐arid floodplain lakeside plant assemblage for meeting the management goal of increasing water‐dependant taxa and functional groups. We developed a multi‐taxon Bayesian hierarchical model to describe temporal and small‐scale spatial patterns in taxonomic occurrences. We then examined community summary metrics (proportion of native taxa, plant functional groups) to evaluate patterns for the entire floodplain lakes system, the scale most relevant to management. Overall, in a system dominated by terrestrial dry plant taxa, 52.9% of terrestrial damp plant taxa showed a short‐term increase in occurrence in response to the environmental flow, which translated into similar responses in some functional groups. However, nearly half of the plant taxa that increased then demonstrated a decline by 18 months after the flow event. Our community summary metrics captured these general results; however, they were disproportionately influenced by a few abundant plant taxa. These results highlight the advantages of multi‐taxon models for interpreting flow responses and developing effective environmental flow management strategies, because they can be used to summarise community responses, while preserving important taxon‐specific information. In semi‐arid systems, where river regulation and climate change have reduced the frequency of flood events, the ability to deliver environmental flows during protracted periods of drought may be a policy option to restore or maintain the natural floodplain vegetation assemblage and prevents the transition to dryland taxa.

Semi-Automated Delineation of Stands in an Even-Age Dominated Forest: A LiDAR-GEOBIA Two-Stage Evaluation Strategy

Regional scale maps of homogeneous forest stands are valued by forest managers and are of interest for landscape and ecological modelling. Research focused on stand delineation has substantially increased in the last decade thanks to the development of Geographic Object Based Image Analysis (GEOBIA). Nevertheless, studies focused on even-age dominated forests are still few and the proposed approaches are often heuristic, local, or lacking objective evaluation protocols. In this study, we present a two-stage evaluation strategy combining both unsupervised and supervised evaluation methods for semi-automatic delineation of forest stands at regional scales using Light Detection and Ranging (LiDAR) raster summary metrics. The methodology is demonstrated on two contiguous LiDAR datasets covering more than 54,000 ha in central Idaho, where clearcuts were a common harvesting method during the twentieth century. Results show good delineation of even-aged forests and demonstrate the ability of LiDAR to discriminate stands harvested more than 50 years ago, that are generally challenging to discriminate with optical data. The two-stage strategy reduces the reference data required within the supervised evaluation and increases the scope of a reliable semi-automatic delineation to larger areas. This is an objective and straightforward approach that could potentially be replicated and adapted to address other study needs.

Using DNA methylation to validate an electronic medical record phenotype for smoking.

A validated, scalable approach to characterizing (phenotyping) smoking status is needed to facilitate genetic discovery. Using established DNA methylation sites from blood samples as a criterion standard for smoking behavior, we compare three candidate electronic medical record (EMR) smoking metrics based on longitudinal EMR text notes. With data from the Veterans Aging Cohort Study (VACS), we employed a validated algorithm to translate each smoking-related text note into current, past or never categories. We compared three alternative summary characterizations of smoking: most recent, modal and trajectories using descriptive statistics and Spearman's correlation coefficients. Logistic regression and area under the curve analyses were used to compare the associations of these phenotypes with the DNA methylation sites, cg05575921 and cg03636183, which are known to have strong associations with current smoking. DNA methylation data were available from the VACS Biomarker Cohort (VACS-BC), a sub-study of VACS. We also considered whether the associations differed by the certainty of trajectory group assignment (<0.80/≥0.80). Among 140152 VACS participants, EMR summary smoking phenotypes varied in frequency by the metric chosen: current from 33 to 53 percent; past from 16 to 24 percent and never from 24 to 33 percent. The association between the EMR smoking pairs was highest for modal and trajectories (rho=0.89). Among 728 individuals in the VACS-BC, both DNA methylation sites were associated with all three EMR summary metrics (p<0.001), but the strongest association with both methylation sites was observed for trajectories (p<0.001). Longitudinal EMR smoking data support using a summary phenotype, the validity of which is enhanced when data are integrated into statistical trajectories.

Comparing apples to oranges: Interpreting ozone concentrations from observational studies in the context of the United States ozone regulatory standard

In 2015, the United States Environmental Protection Agency (US EPA) set the ozone National Ambient Air Quality Standards (NAAQS) at 0.070 parts per million (ppm), for an annual 4th highest daily 8-hour (h) maximum average concentration, averaged over three years, with compliance based on the monitor with the highest concentrations. Numerous epidemiological studies have evaluated associations between ozone and health effects, but how the ozone concentrations derived from those studies can be compared to the ozone NAAQS is not clear, because of the complexity of the standard. The purpose of the present work was to determine how ozone summary metrics used in key epidemiology studies compare to the metrics that comprise the ozone regulatory value. Evaluation of epidemiology studies used for quantitative risk assessment in the 2015 ozone NAAQS review demonstrated that the most commonly used summary metrics that differed from the NAAQS were: 1-h maximum or 24-h average concentrations; multiple-day averages from 2 to 30 days; and averaging of ozone concentrations across all monitors in an area and over different months of the year. Using different ozone summary metrics to calculate the ozone regulatory value in twelve US cities for 2000–2002 or 2013–2015 generated alternative ozone regulatory values that were often substantively different and that may or may not vary commensurate with the regulatory standard. Comparison of epidemiology study metrics to other countries' ozone standards or guideline levels produces similar challenges as described here for the NAAQS. In conclusion, many of the ozone concentration metrics used in epidemiology studies cannot be directly compared to the ozone NAAQS, and using simple conversion ratios adds substantial uncertainty to concentration estimates. These summary metrics must be reconciled to the regulatory value before any judgements are made as to the protectiveness of current and alternative standards based on epidemiology study results.

A hybrid simulation framework for computer simulation and modelling studies of grating-based x-ray phase-contrast images

Clinical studies performed using computer simulation are inexpensive, flexible methods that can be used to study aspects of a proposed imaging technique prior to a full clinical study. Typically, lesions are simulated into (experimental) data to assess the clinical potential of new methods or algorithms. In grating-based phase-contrast imaging (GB-PCI), full wave simulations are, however, computationally expensive due to the high periodicity of the gratings and therefore not practically applicable when large data sets are required. This work describes the development of a hybrid modelling platform that combines analytical and empirical input data for a more rapid simulation of GB-PCI images with little loss of accuracy. Instead of an explicit implementation of grating details, measured summary metrics (i.e. visibility, flux, noise power spectra, presampling modulation transfer function) are applied in order to generate transmission and differential phase images with large fields of view. Realistic transmission and differential phase images were obtained with good quantitative accuracy. The different steps of the simulation framework, as well as the methods to measure the summary metrics, are discussed in detail such that the technique can be easily customized for a given system. The platform offers a fast, accurate alternative to full wave simulations when the focus switches from grating/system design and set up to the generation of GB-PCI images for an established system.

Beyond Metrics? The Role of Hydrologic Baseline Archetypes in Environmental Water Management.

Balancing ecological and human water needs often requires characterizing key aspects of the natural flow regime and then predicting ecological response to flow alterations. Flow metrics are generally relied upon to characterize long-term average statistical properties of the natural flow regime (hydrologic baseline conditions). However, some key aspects of hydrologic baseline conditions may be better understood through more complete consideration of continuous patterns of daily, seasonal, and inter-annual variability than through summary metrics. Here we propose the additional use of high-resolution dimensionless archetypes of regional stream classes to improve understanding of baseline hydrologic conditions and inform regional environmental flows assessments. In an application to California, we describe the development and analysis of hydrologic baseline archetypes to characterize patterns of flow variability within and between stream classes. We then assess the utility of archetypes to provide context for common flow metrics and improve understanding of linkages between aquatic patterns and processes and their hydrologic controls. Results indicate that these archetypes may offer a distinct and complementary tool for researching mechanistic flow-ecology relationships, assessing regional patterns for streamflow management, or understanding impacts of changing climate.

Investigating the Intersession Reliability of Dynamic Brain-State Properties.

Dynamic functional connectivity metrics have much to offer to the neuroscience of individual differences of cognition. Yet, despite the recent expansion in dynamic connectivity research, limited resources have been devoted to the study of the reliability of these connectivity measures. To address this, resting-state functional magnetic resonance imaging data from 100 Human Connectome Project subjects were compared across 2 scan days. Brain states (i.e., patterns of coactivity across regions) were identified by classifying each time frame using k means clustering. This was done with and without global signal regression (GSR). Multiple gauges of reliability indicated consistency in the brain-state properties across days and GSR attenuated the reliability of the brain states. Changes in the brain-state properties across the course of the scan were investigated as well. The results demonstrate that summary metrics describing the clustering of individual time frames have adequate test/retest reliability, and thus, these patterns of brain activation may hold promise for individual-difference research.

Elsewhere in Australia: a snapshot of temporary mobility on the night of the 2016 Census

Background Temporary population mobility, moves of more than one night’s duration that do not entail a change in usual residence, are an important feature of the Australian population surface. The ABS Census of Population and Housing (Census) provides a snapshot of temporary movements one night every five years. Aims This paper examines the intensity, age and spatial patterns of temporary movements captured at the 2016 Census, and creates a classification of regions based on the age profile of movers on Census nights. Data and methods 2016 Census data were extracted using ABS TableBuilder Pro. Summary metrics were calculated to measure the intensity and age profile of movements. Origin–destination flows were derived from a cross-classification of data on Place of Usual Residence and Place of Enumeration. A classification of regions (SA4s) was constructed from the age profile of movers at origins and at destinations. Results 1,142,005 individuals (about 5 per cent of the Australian population) were enumerated away from home on Census night 2016. Mobility peaked in younger (20–30) and older (65–70) age groups. Most movements were between capital city regions; however, resource regions and coastal areas were also implicated. The mobility surface was segmented by age: younger people dominated visits to cities and older movers comprise the majority of visitors to coastal areas, while remote areas had a significant proportion of visitors in the peak working ages. Conclusions Temporary population mobility is selective by age and sex and geographically segmented by these characteristics. Improved understanding of the attribute of visitors to regions can assist to formulate and validate estimates of temporary populations from emerging data sets.

The Child and Adolescent Services Assessment: Interrater Reliability and Predictors of Rater Disagreement.

The current study evaluated the interrater reliability of the Child and Adolescent Services Assessment (CASA), a widely used structured interview measuring pediatric mental health service use. Interviews (N = 72) were randomly selected from a pediatric effectiveness trial, and audio was coded by an independent rater. Regressions were employed to identify predictors of rater disagreement. Interrater reliability was high for items (> 94%) and summary metrics (ICC > .79) across service sectors. Predictors of disagreement varied by domain; significant predictors indexed higher clinical severity or social disadvantage. Results support the CASA as a reliable and robust assessment of pediatric service use, but administrators should be alert when assessing vulnerable populations.

The complex relationship between climate and sugar maple health: Climate change implications in Vermont for a key northern hardwood species

This study compared 141 ecologically relevant climate metrics to field assessments of sugar maple (Acer saccharum Marsh.) canopy condition across Vermont, USA from 1988 to 2012. After removing the influence of disturbance events during this time period to isolate the impact of climate, we identified five climate metrics that were significantly related to sugar maple crown condition. While three of these are monthly summary metrics commonly used in climate analyses (minimum April, August and October temperatures), two are novel metrics designed to capture extreme climate events (periods of unusual warmth in January and August). The proportion of climate-driven variability in canopy condition is comparable to the proportion accounted for by defoliating pests and other disturbance events. This indicates that climate conditions, though rarely included in sugar maple decline studies, may be of equal importance as more traditionally studied stress agents. Modeled across the state, results indicate that changes in historical climatic conditions have negatively impacted sugar maple health over the 25 year study period, and are likely to degrade further over time. Climate projections under a low emissions scenario indicated that by 2071 55% of sugar maple across the state would likely experience moderate to severe climate-driven stress relative to historic baselines, increasing to 84% under a high emissions scenario. However, geographic variability in projected climate impacts indicates that while conditions for sugar maple will likely deteriorate across the state, climate refugia should also be available to maintain sugar maple in spite of changing climatic conditions. Considering the predominant role of sugar maple in Vermont’s economy and culture, managing this resource into the future could pose a considerable challenge.

Summarizing performance for genome scale measurement of miRNA: reference samples and metrics

BackgroundThe potential utility of microRNA as biomarkers for early detection of cancer and other diseases is being investigated with genome-scale profiling of differentially expressed microRNA. Processes for measurement assurance are critical components of genome-scale measurements. Here, we evaluated the utility of a set of total RNA samples, designed with between-sample differences in the relative abundance of miRNAs, as process controls.ResultsThree pure total human RNA samples (brain, liver, and placenta) and two different mixtures of these components were evaluated as measurement assurance control samples on multiple measurement systems at multiple sites and over multiple rounds. In silico modeling of mixtures provided benchmark values for comparison with physical mixtures. Biomarker development laboratories using next-generation sequencing (NGS) or genome-scale hybridization assays participated in the study and returned data from the samples using their routine workflows. Multiplexed and single assay reverse-transcription PCR (RT-PCR) was used to confirm in silico predicted sample differences. Data visualizations and summary metrics for genome-scale miRNA profiling assessment were developed using this dataset, and a range of performance was observed. These metrics have been incorporated into an online data analysis pipeline and provide a convenient dashboard view of results from experiments following the described design. The website also serves as a repository for the accumulation of performance values providing new participants in the project an opportunity to learn what may be achievable with similar measurement processes.ConclusionsThe set of reference samples used in this study provides benchmark values suitable for assessing genome-scale miRNA profiling processes. Incorporation of these metrics into an online resource allows laboratories to periodically evaluate their performance and assess any changes introduced into their measurement process.

Quantifying spatial and temporal patterns of flow intermittency using spatially contiguous runoff data

River channel drying caused by intermittent stream flow is a widely-recognized factor shaping stream ecosystems. There is a strong need to quantify the distribution of intermittent streams across catchments to inform management. However, observational gauge networks provide only point estimates of streamflow variation. Increasingly, this limitation is being overcome through the use of spatially contiguous estimates of the terrestrial water-balance, which can also assist in estimating runoff and streamflow at large-spatial scales. Here we proposed an approach to quantifying spatial and temporal variation in monthly flow intermittency throughout river networks in eastern Australia. We aggregated gridded (5 × 5 km) monthly water-balance data with a hierarchically nested catchment dataset to simulate catchment runoff accumulation throughout river networks from 1900 to 2016. We also predicted zero flow duration for the entire river network by developing a robust predictive model relating measured zero flow duration (% months) to environmental predictor variables (based on 43 stream gauges). We then combined these datasets by using the predicted zero flow duration from the regression model to determine appropriate ‘zero’ flow thresholds for the modelled discharge data, which varied spatially across the catchments examined. Finally, based on modelled discharge data and identified actual zero flow thresholds, we derived summary metrics describing flow intermittency across the catchment (mean flow duration and coefficient-of-variation in flow permanence from 1900 to 2016). We also classified the relative degree of flow intermittency annually to characterise temporal variation in flow intermittency. Results showed that the degree of flow intermittency varied substantially across streams in eastern Australia, ranging from perennial streams flowing permanently (11–12 months) to strongly intermittent streams flowing 4 months or less of year. Results also showed that the temporal extent of flow intermittency varied dramatically inter-annually from 1900 to 2016, with the proportion of intermittent (weakly and strongly intermittent) streams ranging in length from 3% to nearly 100% of the river network, but there was no evidence of an increasing trend towards flow intermittency over this period. Our approach to generating spatially explicit and catchment-wide estimates of streamflow intermittency can facilitate improved ecological understanding and management of intermittent streams in Australia and around the world.

Sensitivity of the Eocene climate to CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; and orbital variability

Abstract. The early Eocene, from about 56 Ma, with high atmospheric CO2 levels, offers an analogue for the response of the Earth's climate system to anthropogenic fossil fuel burning. In this study, we present an ensemble of 50 Earth system model runs with an early Eocene palaeogeography and variation in the forcing values of atmospheric CO2 and the Earth's orbital parameters. Relationships between simple summary metrics of model outputs and the forcing parameters are identified by linear modelling, providing estimates of the relative magnitudes of the effects of atmospheric CO2 and each of the orbital parameters on important climatic features, including tropical–polar temperature difference, ocean–land temperature contrast, Asian, African and South (S.) American monsoon rains, and climate sensitivity. Our results indicate that although CO2 exerts a dominant control on most of the climatic features examined in this study, the orbital parameters also strongly influence important components of the ocean–atmosphere system in a greenhouse Earth. In our ensemble, atmospheric CO2 spans the range 280–3000 ppm, and this variation accounts for over 90 % of the effects on mean air temperature, southern winter high-latitude ocean–land temperature contrast and northern winter tropical–polar temperature difference. However, the variation of precession accounts for over 80 % of the influence of the forcing parameters on the Asian and African monsoon rainfall, and obliquity variation accounts for over 65 % of the effects on winter ocean–land temperature contrast in high northern latitudes and northern summer tropical–polar temperature difference. Our results indicate a bimodal climate sensitivity, with values of 4.36 and 2.54 ∘C, dependent on low or high states of atmospheric CO2 concentration, respectively, with a threshold at approximately 1000 ppm in this model, and due to a saturated vegetation–albedo feedback. Our method gives a quantitative ranking of the influence of each of the forcing parameters on key climatic model outputs, with additional spatial information from singular value decomposition providing insights into likely physical mechanisms. The results demonstrate the importance of orbital variation as an agent of change in climates of the past, and we demonstrate that emulators derived from our modelling output can be used as rapid and efficient surrogates of the full complexity model to provide estimates of climate conditions from any set of forcing parameters.

Standards of Evidence for Conducting and Reporting Economic Evaluations in Prevention Science

Over a decade ago, the Society for Prevention Research endorsed the first standards of evidence for research in preventive interventions. The growing recognition of the need to use limited resources to make sound investments in prevention led the Board of Directors to charge a new task force to set standards for research in analysis of the economic impact of preventive interventions. This article reports the findings of this group’s deliberations, proposes standards for economic analyses, and identifies opportunities for future prevention science. Through examples, policymakers’ need and use of economic analysis are described. Standards are proposed for framing economic analysis, estimating costs of prevention programs, estimating benefits of prevention programs, implementing summary metrics, handling uncertainty in estimates, and reporting findings. Topics for research in economic analysis are identified. The SPR Board of Directors endorses the “Standards of Evidence for Conducting and Reporting Economic Evaluations in Prevention Science.”

Stimulus characteristics in electroconvulsive therapy. A pragmatic review

The process of normalization electroconvulsive therapy (ECT) requires, among other actions, disseminating the latest information on this technique. One of the most complex aspects is the electrical stimulus, whose knowledge should be spread and put into practice.In this paper, we review the available information about frequency and number of ECT sessions, and efficacy of each electrode placement. We also present two approaches to determine the ECT charge: stimulus titration versus age-based method; and the limitations of the summary metrics of charge, being necessary to expand our knowledge of the parameters that configure the stimulus: duration, current amplitude frequency and pulse width.

National Quality Assurance &amp; Improvement System (NQAIS) – Medicine

Introduction: Length of stay varies by hospital, admission type, diagnosis and clinical team, while waiting lists, emergency department throughput and delayed discharges generate frustration for patients and health professionals. The National Acute Medical Programme (NAMP) was established in 2010 by the Royal College of Physicians of Ireland and the Health Service Executive (HSE) to enable: - Swifter access to senior decision makers - Swifter access to investigations and interventions - Reduced overnight admissions - Shorter average length of stay (AvLOS) - Optimal use of Acute Medical Assessment Unit (AMAU) NQAIS Medicine: NQAIS Medicine supports NAMP and hospitals to re-engineer care delivery based on interdisciplinary co-operation using timely and relevant data. The system was developed by Health Intelligence, Health & Wellbeing, Health Service Executive, in collaboration with NAMP and OpenApp. It is deployed under the governance framework of the Joint NQAIS (Surgery & Medicine) Steering Group. NQAIS Medicine is a web-enabled, role-based quality assurance and improvement system providing an evidence base to inform decisions. Innovative “diamond” plots (simplified box and whisker) and trend plots in easily understood “ribbons” are displayed by hospital (or group), admission category, diagnosis and clinical team. Patient profiles (age, co-morbidity, admission day/time etc.) can be flexibly explored. Selected records can be identified for chart review. The predictive impact of meeting targets are summarised by the novel computation of the potential “number of beds freed per day” which could be made available for other patients. The Clinical Classification System (CCS) - Agency for Healthcare Research & Quality (AHRQ) - converts the principal (admission) diagnosis into clinically meaningful groups. The Charlson Index is used to convert secondary diagnoses into a “complexity” score. AvLOS and readmission rates are dynamically analysed in the context of data-driven targets: AvLOS per CCS diagnosis as achieved by top quartile (Irish) teams; 50%+ all acute medical admissions should be routed via the AMAU of whom 25% should be discharged home. AvLOS is computed for each CCS nationally in the context of its trim point - national 75%ile + 3 X (25% – 75%ile). If the target AvLOS is achieved and AMAU usage optimised, the potential impact is translated into simple summary metrics - beds used within, close to and off target. These metrics identify and prioritise processes in hospital most likely to reveal potential learning and reengineering opportunities for using beds for other patients. Implementation: A train-the-trainers approach within hospital groups, guided by a national training team, supports the national deployment of the system. NQAIS Medicine combines science and art to provide detailed but very understandable feedback to unequivocally identify areas for review. Modest meeting of targets would transform a system struggling to cope with increasing demands. The successful implementation of NAMP, using NQAIS Medicine as a continuous improvement enabler, requires sustained focus and commitment from clinicians and management alike. The system is currently being deployed to all acute hospital in Ireland, and is being extended to encompass surgery and other domains within a single system referred to as NQAIS Clinical.

Negative Influence of Motor Impairments on Upper Limb Movement Patterns in Children with Unilateral Cerebral Palsy. A Statistical Parametric Mapping Study.

Upper limb three-dimensional movement analysis (UL-3DMA) offers a reliable and valid tool to evaluate movement patterns in children with unilateral cerebral palsy (uCP). However, it remains unknown to what extent the underlying motor impairments explain deviant movement patterns. Such understanding is key to develop efficient rehabilitation programs. Although UL-3DMA has been shown to be a useful tool to assess movement patterns, it results in a multitude of data, challenging the clinical interpretation and consequently its implementation. UL-3DMA reports are often reduced to summary metrics, such as average or peak values per joint. However, these metrics do not take into account the continuous nature of the data or the interdependency between UL joints, and do not provide phase-specific information of the movement pattern. Moreover, summary metrics may not be sensitive enough to estimate the impact of motor impairments. Recently, Statistical Parametric Mapping (SPM) was proposed to overcome these problems. We collected UL-3DMA of 60 children with uCP and 60 typically developing children during eight functional tasks and evaluated the impact of spasticity and muscle weakness on UL movement patterns. SPM vector field analysis was used to analyze movement patterns at the level of five joints (wrist, elbow, shoulder, scapula, and trunk). Children with uCP showed deviant movement patterns in all joints during a large percentage of the movement cycle. Spasticity and muscle weakness negatively impacted on UL movement patterns during all tasks, which resulted in increased wrist flexion, elbow pronation and flexion, increased shoulder external rotation, decreased shoulder elevation with a preference for movement in the frontal plane and increased trunk internal rotation. Scapular position was altered during movement initiation, although scapular movements were not affected by muscle weakness or spasticity. In conclusion, we identified pathological movement patterns in children with uCP and additionally mapped the negative impact of spasticity and muscle weakness on these movement patterns, providing useful insights that will contribute to treatment planning. Last, we also identified a subset of the most relevant tasks for studying UL movements in children with uCP, which will facilitate the interpretation of UL-3DMA data and undoubtedly contribute to its clinical implementation.

Quantifying person-level brain network functioning to facilitate clinical translation

Although advances in neuroimaging have yielded insights into the intrinsic organization of human brain networks and their relevance to psychiatric and neurological disorders, there has been no translation of these insights into clinical practice. One necessary step toward clinical translation is identifying a summary metric of network function that is reproducible, reliable, and has known normative data, analogous to normed neuropsychological tests. Our aim was therefore to establish the proof of principle for such a metric, focusing on the default mode network (DMN). We compared three candidate summary metrics: global clustering coefficient, characteristic path length, and average connectivity. Across three samples totaling 322 healthy, mostly Caucasian adults, average connectivity performed best, with good internal consistency (Cronbach’s α=0.69–0.70) and adequate eight-week test–retest reliability (intra-class coefficient=0.62 in a subsample N=65). We therefore present normative data for average connectivity of the DMN and its sub-networks. These proof of principle results are an important first step for the translation of neuroimaging to clinical practice. Ultimately, a normed summary metric will allow a single patient’s DMN function to be quantified and interpreted relative to normative peers.