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Supplemental material, sj-pdf-1-nnr-10.1177_15459683231166937 for Walking Outcome After Traumatic Paraplegic Spinal Cord Injury: The Function of Which Myotomes Makes a Difference? by Adrian Cathomen, Doris Maier, Jiri Kriz, Rainer Abel, Frank Röhrich, Michael Baumberger, Giorgio Scivoletto, Norbert Weidner, Rüdiger Rupp, Catherine R. Jutzeler, John D. Steeves, Armin Curt and Marc Bolliger in Neurorehabilitation and Neural Repair

STUDY DESIGN Retrospective analysis of prospectively collected data. OBJECTIVE Central cord syndrome (CCS) is reported to have better outcomes than other cervical lesions, especially for ambulation and bladder recovery. However, a formal comparison between patients with CCS and other incomplete cervical spinal cord injuries (iCSCI) is lacking. Aim of the study is to investigate the neurological and functional outcomes in patients with or without CCS. SETTING European Multicenter Study. METHODS Data following SCI were derived from the European Multicenter Study about Spinal Cord Injury Database. CCS was diagnosed based on a difference of at least ten points of motor score in favour of the lower extremities. Patients were evaluated at 30 days, 6 months and 1 year from injury. The neurological and functional data were collected at each time point based on the International Standards for Neurological Classification of Spinal Cord injury (ISNSCI) and Spinal Cord Independence Measure (SCIM). Patients were selected with a matching procedure based on lesion severity, neurological level of injury (NLI) and age. Evaluation of the outcomes was performed by means of two-way Anova for repeated measures. RESULTS The matching produced 110 comparable dyads. At all time points, upper extremity motor scores remained lower than lower extremity motor scores in CCS compared with iCSCI. With regard to daily life independence, both cohorts achieved comparable improvements in self-care sub-scores between T0 and T2 (6.6 ± 6.5 in CCS vs 8.2 ± 6.9 in iCSCI, p = 0.15) but this sub-score was significantly lower in CCS compared with iCSCI (3.6 ± 5.2 in CCS vs 7.3 ± 7.0 in iCSCI at T0, 13.7 ± 6.2 vs 16.5 ± 5.7 at T2), while the other sub-scores were comparable. CONCLUSIONS In contrast to previous reports, people with CCS have poorer outcomes of self-care ability compared with iCSCI.

Background. The relative rarity of ischemic compared with traumatic spinal cord injury (SCI) has limited a comparison of the outcomes of these conditions. Objective. To investigate the neurological and functional recovery of ischemic compared with traumatic acute SCI. Methods. Data were derived from the European Multicenter Study Spinal Cord Injury database. Patients with ischemic (iSCI) or traumatic SCI (tSCI), aged 18 years or older were evaluated at different time points from incidence: at about 1 month, 3 months, and 6 months. The neurological status was assessed at each time point by the International Standards for Neurological Classification of Spinal Cord Injury and the functional status by the Spinal Cord Independence Measure. Walking ability was evaluated by Walking Index for Spinal Cord Injury, 10-Meter Walk Test, and 6-Minute Walk Test. Because of the imbalances of the 2 groups in respect to size and lesion severity, a matching procedure according to age, neurological level, and severity of injury was performed. Outcomes evaluation was performed by means of a 2-way repeated-measures ANOVA. Results. The matching procedure resulted in 191 pairs. Both groups significantly improved from about 15 days after the lesion to 6 months. No differences were found in the course of neurological and functional recovery of iSCI compared with tSCI. Conclusions. This analysis from a representative cohort of participants revealed that from 15 days following the cord damage onward, the outcomes after iSCI and tSCI are comparable. This finding supports the potential enrolment of patients with acute iSCI into clinical trials from that point in time after the event and an evaluation up to 6 months afterward.

SS (Strategic Scanning) is unique, partially subjective, inconsistent, interdisciplinary, vague and multidimensional process. Its description and optimisation suffers from IS (Information Shortage) and heterogeneity. IS eliminates straightforward application of traditional statistical methods. Heterogeneity problems are caused by heterogeneous nature of scanned information structures. Artificial Intelligence has developed some tools to solve such problems. Qualitative reasoning is one of them. It is based on the least information intensive quantifiers i.e. trends. There are four different trends i.e. qualitative values and their derivatives: plus/increasing; zero/constant; negative/decreasing; any value / any trend. The paper studies SS models based on ELEs (Equationless Heuristics). An example of ELE is ¿ If novelity is increasing then confidence is decreasing. A solution of a qualitative model is represented by a set S of scenarios and a set T of time transitions among these scenarios. The key information input into an ELE model is subjective knowledge of experts. A consensus among SS experts is often not reached because of inconsistencies of experts¿ knowledge. The SS case study is 12 dimensional (e.g. Freshness, Relevance) and based on 12 ELEs. There are 29 scenarios, SS (Strategic Scanning) es un proceso único, parcialmente subjetivo, inconsistente, interdisciplinario, vago y multidimensional. Su descripción y optimización adolece de IS (escasez de información) y heterogeneidad. IS elimina la aplicación directa de los métodos estadísticos tradicionales. Los problemas de heterogeneidad son causados por la naturaleza heterogénea de las estructuras de información escaneadas. La Inteligencia Artificial ha desarrollado algunas herramientas para resolver tales problemas. El razonamiento cualitativo es uno de ellos. Se basa en los cuantificadores que requieren menos información, es decir, las tendencias. Hay cuatro tendencias diferentes, es decir, valores cualitativos y sus derivados: más / creciente; cero / constante; negativo / decreciente; cualquier valor / cualquier tendencia. El artículo estudia los modelos SS basados en ELE (heurística sin ecuaciones). Un ejemplo de ELE es: si la novedad aumenta, la confianza disminuye. La solución de un modelo cualitativo está representada por un conjunto S de escenarios y un conjunto T de transiciones de tiempo entre estos escenarios. La entrada de información clave en un modelo ELE es el conocimiento subjetivo de los expertos. A menudo no se llega a un consenso entre los expertos en SS debido a inconsistencias en el conocimiento de los expertos. El estudio de caso de SS es de 12 dimensiones (por ejemplo, Frescura, Relevancia) y se basa en 12 ELE. Hay 29 escenarios., Universidad Pablo de Olavide

Project management is characterize like the broader concept of a comprehensive set of management processes and activities that are limited in time and whose aim is to implement something specific, whether the introduction, change, etc. In project management, which aims to ensure effective management of a comprehensive package of activities to a greater or lesser extent, concerns virtually all organizations and from internal changes or activities, supply of products, the introduction of ICT technologies to large investment projects. Project management involves the application of knowledge, experience, skills, activities, tools and techniques so that the final project met its requirements and achieves its goals in a limited time interval. Between the initial and final state the project goes through several phases, including project risk. To eliminate these risks is determined by the risk management as an area focusing on analysis and risk reduction using various tools and techniques. If we seek to answer the question what is the risk, then in terms of project management it can be understood as the likelihood that an event occurs that is contrary to the assumption. The first stage is to identify risks. This is based on the areas covered by the project and cannot be generalized for different types of projects. For example, a project for the implementation of data warehouse will have different areas of risk than new product development. The next stage is risk analysis. At this stage, we try to find the level of risk and its impact on the completion of the project. We are looking for those risks which are important and have a significant influence on the project (priority risks). Following the planning and risk management, which proposes procedures to minimize risk, responsibility for the procedures and time frames in which the procedures are being implemented. The last phase is monitoring, which leads to elimination of risks, which are no longer relevant and to re-identify new risks. This entire process is appropriate to support software tool that allows us to their effective management. We can use Business Intelligence tools as one of the software tools, especially in the phase of risk identification and analysis. Identifying risks putting together a basic set of potential risks when the input use various available sources of information such as the previously identified risks files or lists the usual risks in managing similar projects. In the analysis phase, then we can make risk assessment of the potential risks, including the determination of their probabilities to create a catalogue of potential risks of the project, which must be addressed at the planning stage and management, Business Intelligence tools are with justification used for the suggestions to minimize risks. The article discusses the Business Intelligence tools and their application in the field of project's risk management. This is an opportunity to create panels, tables, graphs and matrices, including analyses of data cubes and to a certain extent and use of prediction algorithms for determining the probability of the risk and its impact on the implementation of the project.

International audience

ISBN:3-260-04170-2