Indicators used by clinicians to assess pain in the brain injured

INTRODUCTION. The assessment of pain in critically ill brain-injured patients is challenging for health professionals. In addition to be unable to self-report, the confused and stereotyped behaviors of these patients are likely to alter their ''normal'' pain responses. Therefore, the pain indicators observed in the general critically ill population may not be appropriate. OBJECTIVES. To identify behavioral and physiological indicators used by clinicians to assess pain in critically ill brain-injured patients who are unable to self-report. METHODS.Amixed-method design was used with the first step being the combination of the results of an integrative literature review with the results of nominal groups of 12 nurses and four physicians. The second step involved a web-based survey to establish content validity. Fourteen experts (clinicians and academics) from three French speaking European countries rated the relevance of each indicator. A content validity index (CVI) was computed for each indicator (I-CVI) and for each category (S-CVI). RESULTS. The first step generated 52 indicators. These indicators were classified into six categories: facial expressions, position/movement, muscle tension, vocalization, compliance with ventilator, and physiological indicators. In the second step, the agreement between raters was high with an Intraclass Correlation Coefficient of 0.88 (95% CI 0.83-0.92). The I-CVIs ranged from 0.07 to 1. Indicators with an I-CVI below 0.5 (n = 12) were not retained, resulting in a final list of 30 indicators. The CVI for this final list was 0.75 with categories ranging from 0.67 (compliance with ventilation) to 0.87 (vocalization). CONCLUSIONS. This process identified specific pain indicators for critically ill braininjured patients. Further evaluation is in progress to test the validity and relevance of these indicators in the clinical setting.

Iowa Leading Indicators Index after One Year: An Assessment and Update

The Iowa Leading Indicators Index (ILII) Annual Assessment and Update assesses how well the ILII has met the goals behind its development, gauges the validity of the existing components, considers additional components that have been suggested along the way, and carries out the annual updates necessary for such an index.

Iowa Leading Indicators Index after Two Years: An Assessment and Update

The Iowa Leading Indicators Index (ILII) Annual Assessment and Update assesses how well the ILII has met the goals behind its development, gauges the validity of the existing components, considers additional components that have been suggested along the way, and carries out the annual updates necessary for such an index.

Iowa Leading Indicators Index after Three Years: An Assessment and Update

The Iowa Leading Indicators Index (ILII) Annual Assessment and Update assesses how well the ILII has met the goals behind its development, gauges the validity of the existing components, considers additional components that have been suggested along the way, and carries out the annual updates necessary for such an index.

Iowa Leading Indicators Index July 2009

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index, August 2009

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Ecological temporal stability of Staphylococcus aureus nasal carriage.

We described the colonization dynamics of Staphylococcus aureus in a group of 266 healthy carriers over a period of approximately 1 year. We used precise genotyping methods, i.e., amplified fragment length polymorphism (AFLP), spa typing, and double-locus sequence typing (DLST), to detect changes in strain identity. Strain change took place rather rarely: out of 89 carriers who had initially been colonized, only 7 acquired a strain different from the original one. Approximately one-third of the carriers eliminated the colonization, and a similar number became newly colonized. Some of these events probably represent detection failure rather than genuine colonization loss or acquisition. Lower bacterial counts were associated with increased probability of eliminating the colonization. We have confirmed a high mutation rate in the spa locus: 6 out of 53 strains underwent mutation in the spa locus. There was no overall change in S. aureus genotype composition.

Ecological assembly rules in plant communities--approaches, patterns and prospects.

Understanding how communities of living organisms assemble has been a central question in ecology since the early days of the discipline. Disentangling the different processes involved in community assembly is not only interesting in itself but also crucial for an understanding of how communities will behave under future environmental scenarios. The traditional concept of assembly rules reflects the notion that species do not co-occur randomly but are restricted in their co-occurrence by interspecific competition. This concept can be redefined in a more general framework where the co-occurrence of species is a product of chance, historical patterns of speciation and migration, dispersal, abiotic environmental factors, and biotic interactions, with none of these processes being mutually exclusive. Here we present a survey and meta-analyses of 59 papers that compare observed patterns in plant communities with null models simulating random patterns of species assembly. According to the type of data under study and the different methods that are applied to detect community assembly, we distinguish four main types of approach in the published literature: species co-occurrence, niche limitation, guild proportionality and limiting similarity. Results from our meta-analyses suggest that non-random co-occurrence of plant species is not a widespread phenomenon. However, whether this finding reflects the individualistic nature of plant communities or is caused by methodological shortcomings associated with the studies considered cannot be discerned from the available metadata. We advocate that more thorough surveys be conducted using a set of standardized methods to test for the existence of assembly rules in data sets spanning larger biological and geographical scales than have been considered until now. We underpin this general advice with guidelines that should be considered in future assembly rules research. This will enable us to draw more accurate and general conclusions about the non-random aspect of assembly in plant communities.

Iowa Leading Indicators Index September 2009

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index October 2009

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index, November 2009

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index, December 2009

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index January 2010

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

Iowa Leading Indicators Index February 2010

The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.