Evaluation of an integrated system for classification, assessment and comparison of services for long-term care in Europe: the eDESDE-LTC study

Background: The harmonization of European health systems brings with it a need for tools to allow the standardized collection of information about medical care. A common coding system and standards for the description of services are needed to allow local data to be incorporated into evidence-informed policy, and to permit equity and mobility to be assessed. The aim of this project has been to design such a classification and a related tool for the coding of services for Long Term Care (DESDE-LTC), based on the European Service Mapping Schedule (ESMS). Methods: The development of DESDE-LTC followed an iterative process using nominal groups in 6 European countries. 54 researchers and stakeholders in health and social services contributed to this process. In order to classify services, we use the minimal organization unit or “Basic Stable Input of Care” (BSIC), coded by its principal function or “Main Type of Care” (MTC). The evaluation of the tool included an analysis of feasibility, consistency, ontology, inter-rater reliability, Boolean Factor Analysis, and a preliminary impact analysis (screening, scoping and appraisal). Results: DESDE-LTC includes an alpha-numerical coding system, a glossary and an assessment instrument for mapping and counting LTC. It shows high feasibility, consistency, inter-rater reliability and face, content and construct validity. DESDE-LTC is ontologically consistent. It is regarded by experts as useful and relevant for evidence-informed decision making. Conclusion: DESDE-LTC contributes to establishing a common terminology, taxonomy and coding of LTC services in a European context, and a standard procedure for data collection and international comparison.

Long time remodeling during retinal degeneration evaluated by optical coherence tomography, immunocytochemistry and fundus autofluorescence

Purpose: To characterize the relationship between fundus autofluorescence (FAF), Optical Coherence Tomography (OCT) and immunohistochemistry (IHC) over the course of chronic retinal degeneration in the P23H rat. Methods: Homozygous albino P23H rats, Sprague–Dawley (SD) rats as controls and pigmented Long Evans (LE) rats were used. A Spectralis HRA OCT system was used for scanning laser ophthalmoscopy (SLO) imaging OCT and angiography. To determine FAF, fluorescence was excited using diode laser at 488 nm. A fast retina map OCT was performed using the optic nerve as a landmark. IHC was performed to correlate with the findings of OCT and FAF changes. Results: During the course of retinal degeneration, the FAF pattern evolved from some spotting at 2 months old to a mosaic of hyperfluorescent dots in rats 6 months and older. Retinal thicknesses progressively diminished over the course of the disease. At later stages of degeneration, OCT documented changes in the retinal layers, however, IHC better identified the cell loss and remodeling changes. Angiography revealed attenuation of the retinal vascular plexus with time. Conclusion: We provide for the first time a detailed long-term analysis of the course of retinal degeneration in P23H rats using a combination of SLO and OCT imaging, angiography, FAF and IHC. Although, the application of noninvasive methods enables longitudinal studies and will decrease the number of animals needed for a study, IHC is still an essential tool to identify retinal changes at the cellular level.

A connectionist computational method for face recognition

In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.