Articles: pain-management-methods.
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Ankylosing spondylitis (AS) is a chronic inflammatory arthritis which causes potentially debilitating pain and loss of mobility. Biologics represent a highly effective treatment option in AS. Nonetheless, the choice of biologics often involves complex decision-making. ⋯ Analysis of the qualitative data highlighted three key aspects of the MCA; the usefulness of the MCA, the need to present concise and relevant content; and the importance of an intuitively designed tool. Overall, participants found the MCA to be potentially valuable in supporting the current unmet needs in clinical care and had expressed a willingness to use the MCA. The MCA had great potential in supporting shared decision-making by improving patients' knowledge on disease and treatment options, as well as clarifying patients' personal preferences and values in the management of AS.
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Sham interventions in randomized clinical trials (RCTs) of physical, psychological, and self-management (PPS) therapies for pain are highly variable in design and believed to contribute to poor internal validity. However, it has not been formally tested whether the extent to which sham controls resemble the treatment under investigation consistently affects trial outcomes, such as effect sizes, differential attrition, participant expectancy, and blinding effectiveness. Placebo- or sham-controlled RCTs of PPS interventions of clinical pain populations were searched in 12 databases. ⋯ The results support the supposed link between blinding methods and effect sizes, based on a large and systematically sourced overview of methods. However, challenges to effective blinding are complex and often difficult to discern from trial reports. Nonetheless, these insights have the potential to change trial design, conduct, and reporting and will inform guideline development.
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Blinding is challenging in randomised controlled trials of physical, psychological, and self-management therapies for pain, mainly because of their complex and participatory nature. To develop standards for the design, implementation, and reporting of control interventions in efficacy and mechanistic trials, a systematic overview of currently used sham interventions and other blinding methods was required. Twelve databases were searched for placebo or sham-controlled randomised clinical trials of physical, psychological, and self-management treatments in a clinical pain population. ⋯ We also provide an overview of additional, potentially useful methods to enhance blinding, as well as the reporting of processes involved in developing control interventions. A comprehensive picture of prevalent blinding methods is provided, including a detailed assessment of the resemblance between active and control interventions. These findings can inform future developments of control interventions in efficacy and mechanistic trials and best-practice recommendations.
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Evidence-based medicine is replete with studies assessing quality and bias, but few evaluating research integrity or trustworthiness. A recent Cochrane review of psychological interventions for chronic pain identified trials with a shared lead author with highly divergent results. We sought to systematically identify all similar trials from this author to explore their risk of bias, governance procedures, and trustworthiness. ⋯ We discuss the findings within the context of methods for establishing the trustworthiness of research findings generally. Important concerns regarding the trustworthiness of these trials reduce our confidence in them. They should probably not be used to inform the results and conclusions of systematic reviews, in clinical training, policy documents, or any relevant instruction regarding adult chronic pain management.
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Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. ⋯ More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.