This paper proposes an amendment of the classification of safety events based on their controllability and contemplates the potential of an event to escalate into higher severity classes. It considers (1) whether the end-user had the opportunity to intervene into the course of an event, (2) the level of end-user familiarity with the situation, and (3) the positive or negative effects of end-user intervention against expected outcomes. To examine its potential, we applied the refined classification to 296 aviation safety investigation reports. The results suggested that pilots controlled only three-quarters of the occurrences, more than three-thirds of the controlled cases regarded fairly unfamiliar situations, and the flight crews succeeded to mitigate the possible negative consequences of events in about 71% of the cases. Further statistical tests showed that the controllability-related characteristics of events had not significantly changed over time, and they varied across regions, aircraft, operational and event characteristics, as well as when fatigue had contributed to the occurrences. Overall, the findings demonstrated the value of using the controllability classification before considering the actual outcomes of events as means to support the identification of system resilience and successes. The classification can also be embedded in voluntary reporting systems to allow end-users to express the degree of each of the controllability characteristics so that management can monitor them over time and perform internal and external benchmarking. The mandatory reports concerned, the classification could function as a decision-making parameter for prioritising incident investigations.
Purpose: Classification is a defining factor for competition in wheelchair sports, but it is a delicate and time-consuming process with often questionable validity. New inertial sensor-based measurement methods applied in match play and field tests allow for more precise and objective estimates of the impairment effect on wheelchair-mobility performance. The aim of the present research was to evaluate whether these measures could offer an alternative point of view for classification. Methods: Six standard wheelchair-mobility performance outcomes of different classification groups were measured in match play (n = 29), as well as best possible performance in a field test (n = 47). Results: In match results, a clear relationship between classification and performance level is shown, with increased performance outcomes in each adjacent higher-classification group. Three outcomes differed significantly between the low- and mid-classified groups, and 1, between the mid- and high-classified groups. In best performance (field test), there was a split between the low- and mid-classified groups (5 out of 6 outcomes differed significantly) but hardly any difference between the mid- and high-classified groups. This observed split was confirmed by cluster analysis, revealing the existence of only 2 performance-based clusters. Conclusions: The use of inertial sensor technology to obtain objective measures of wheelchair-mobility performance, combined with a standardized field test, produced alternative views for evidence-based classification. The results of this approach provide arguments for a reduced number of classes in wheelchair basketball. Future use of inertial sensors in match play and field testing could enhance evaluation of classification guidelines, as well as individual athlete performance. DOI: https://doi.org/10.1123/ijspp.2017-0326 LinkedIn: https://www.linkedin.com/in/rienkvdslikke/ https://www.linkedin.com/in/moniqueberger/ https://www.linkedin.com/in/annemarie-de-witte-9582b154/
MULTIFILE
Cervical spinal manipulations (CSM) are frequently employed techniques to alleviate neck pain and headache. Minor and major complications following CSM have been described, though clear consensus on definition and the classification of the complications had not yet been achieved. As a result, incidence rates may be underestimated. The aim of this study was to develop a consensus-based classification of adverse events following cervical spinal manipulations which has good feasibility in clinical practice and research. Design: A three round Delphi-study. Medical specialists, manual therapists, and patients (n=30) participated in an online survey. In Round 1, participants were invited to select a classification system of adverse events. Potential complications were inventoried and detailed in accordance with the ICF and the ICD-10. In Round 2, panel members categorized the potential complications in their selected classification. During the third round, it was inquired of the participants whether they concurred with the answer of the majority of participants. Results: Thirty four complications were defined. Consensus was achieved for 29 complications for all durations [hours, days, weeks]. For the remaining five complications, consensus was reached for two of the three durations [hours, days, weeks]. Conclusions: A consensus-based classification system of adverse events after cervical spinal manipulation was developed which comprises patients’ and clinicians’ perspectives and has only a small number of categories. The classification system includes a precise description of potential adverse events and is based on international accepted classifications (ICD-10 and ICF). This classification system may be useful for utilization in both clinical practice and research.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
The pressure on the European health care system is increasing considerably: more elderly people and patients with chronic diseases in need of (rehabilitation) care, a diminishing work force and health care costs continuing to rise. Several measures to counteract this are proposed, such as reduction of the length of stay in hospitals or rehabilitation centres by improving interprofessional and person-centred collaboration between health and social care professionals. Although there is a lot of attention for interprofessional education and collaborative practice (IPECP), the consortium senses a gap between competence levels of future professionals and the levels needed in rehabilitation practice. Therefore, the transfer from tertiary education to practice concerning IPECP in rehabilitation is the central theme of the project. Regional bonds between higher education institutions and rehabilitation centres will be strengthened in order to align IPECP. On the one hand we deliver a set of basic and advanced modules on functioning according to the WHO’s International Classification of Functioning, Disability and Health and a set of (assessment) tools on interprofessional skills training. Also, applications of this theory in promising approaches, both in education and in rehabilitation practice, are regionally being piloted and adapted for use in other regions. Field visits by professionals from practice to exchange experiences is included in this work package. We aim to deliver a range of learning materials, from modules on theory to guidelines on how to set up and run a student-run interprofessional learning ward in a rehabilitation centre. All tested outputs will be published on the INPRO-website and made available to be implemented in the core curricula in tertiary education and for lifelong learning in health care practice. This will ultimately contribute to improve functioning and health outcomes and quality of life of patients in rehabilitation centres and beyond.
Multiple sclerosis (MS) is a severe inflammatory condition of the central nervous system (CNS) affecting about 2.5 million people globally. It is more common in females, usually diagnosed in their 30s and 40s, and can shorten life expectancy by 5 to 10 years. While MS is rarely fatal; its effects on a person's life can be profound, which signifies comprehensive management and support. Most studies regarding MS focus on how lymphocytes and other immune cells are involved in the disease. However, little attention has been given to red blood cells (erythrocytes), which might also be important in developing MS. Artificial intelligence (AI) has shown significant potential in medical imaging for analyzing blood cells, enabling accurate and efficient diagnosis of various conditions through automated image analysis. The project aims to implement an AI pipeline based on Deep Learning (DL) algorithms (e.g., Transfer Learning approach) to classify MS and Healthy Blood cells.
