The Neuropsychiatric Inventory-Clinician rating scale (NPI-C): Reliability and validity of a revised assessment of neuropsychiatric symptoms in dementia

Background: Neuropsychiatric symptoms (NPS) affect almost all patients with dementia and are a major focus of study and treatment. Accurate assessment of NPS through valid, sensitive and reliable measures is crucial. Although current NPS measures have many strengths, they also have some limitations (e.g. acquisition of data is limited to informants or caregivers as respondents, limited depth of items specific to moderate dementia). Therefore, we developed a revised version of the NPI, known as the NPI-C. The NPI-C includes expanded domains and items, and a clinician-rating methodology. This study evaluated the reliability and convergent validity of the NPI-C at ten international sites (seven languages). Methods: Face validity for 78 new items was obtained through a Delphi panel. A total of 128 dyads (caregivers/patients) from three severity categories of dementia (mild = 58, moderate = 49, severe = 21) were interviewed separately by two trained raters using two rating methods: the original NPI interview and a clinician-rated method. Rater 1 also administered four additional, established measures: the Apathy Evaluation Scale, the Brief Psychiatric Rating Scale, the Cohen-Mansfield Agitation Index, and the Cornell Scale for Depression in Dementia. Intraclass correlations were used to determine inter-rater reliability. Pearson correlations between the four relevant NPI-C domains and their corresponding outside measures were used for convergent validity. Results: Inter-rater reliability was strong for most items. Convergent validity was moderate (apathy and agitation) to strong (hallucinations and delusions; agitation and aberrant vocalization; and depression) for clinician ratings in NPI-C domains. Conclusion: Overall, the NPI-C shows promise as a versatile tool which can accurately measure NPS and which uses a uniform scale system to facilitate data comparisons across studies. Copyright © 2010 International Psychogeriatric Association.

Selecting candidate labels for hierarchical document clusters using association rules

One way to organize knowledge and make its search and retrieval easier is to create a structural representation divided by hierarchically related topics. Once this structure is built, it is necessary to find labels for each of the obtained clusters. In many cases the labels have to be built using only the terms in the documents of the collection. This paper presents the SeCLAR (Selecting Candidate Labels using Association Rules) method, which explores the use of association rules for the selection of good candidates for labels of hierarchical document clusters. The candidates are processed by a classical method to generate the labels. The idea of the proposed method is to process each parent-child relationship of the nodes as an antecedent-consequent relationship of association rules. The experimental results show that the proposed method can improve the precision and recall of labels obtained by classical methods. © 2010 Springer-Verlag.

Improving hierarchical document cluster labels through candidate term selection

One way to organize knowledge and make its search and retrieval easier is to create a structural representation divided by hierarchically related topics. Once this structure is built, it is necessary to find labels for each of the obtained clusters. In many cases the labels must be built using all the terms in the documents of the collection. This paper presents the SeCLAR method, which explores the use of association rules in the selection of good candidates for labels of hierarchical document clusters. The purpose of this method is to select a subset of terms by exploring the relationship among the terms of each document. Thus, these candidates can be processed by a classical method to generate the labels. An experimental study demonstrates the potential of the proposed approach to improve the precision and recall of labels obtained by classical methods only considering the terms which are potentially more discriminative. © 2012 - IOS Press and the authors. All rights reserved.

The role of hierarchical morphologies in the superior gas sensing performance of CuO-based chemiresistors

The development of gas sensors with innovative designs and advanced functional materials has attracted considerable scientific interest given their potential for addressing important technological challenges. This work presents new insight towards the development of high-performance p-type semiconductor gas sensors. Gas sensor test devices, based on copper (II) oxide (CuO) with innovative and unique designs (urchin-like, fiber-like, and nanorods), are prepared by a microwave-assisted synthesis method. The crystalline composition, surface area, porosity, and morphological characteristics are studied by X-ray powder diffraction, nitrogen adsorption isotherms, field-emission scanning electron microscopy and high-resolution transmission electron microscopy. Gas sensor measurements, performed simultaneously on multiple samples, show that morphology can have a substantial influence on gas sensor performance. An assembly of urchin-like structures is found to be most effective for hydrogen detection in the range of parts-per-million at 200 °C with 300-fold larger response than the previously best reported values for semiconducting CuO hydrogen gas sensors. These results show that morphology plays an important role in the gas sensing performance of CuO and can be effectively applied in the further development of gas sensors based on p-type semiconductors. High-performance gas sensors based on CuO hierarchical morphologies with in situ gas sensor comparison are reported. Urchin-like morphologies with high hydrogen sensitivity and selectivity that show chemical and thermal stability and low temperature operation are analyzed. The role of morphological influences in p-type gas sensor materials is discussed. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.