Pain
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Pragmatic randomised clinical trials aim to directly inform clinical or health policy decision making. Here, we systematically review methods and design of pragmatic trials of pain therapies to examine methods, identify common challenges, and areas for improvement. Seven databases were searched for pragmatic randomised controlled clinical trials that assessed pain treatment in a clinical population of adults reporting pain. ⋯ They were lowest for patient recruitment methods and extent of follow-up measurements and appointments. Current practice in pragmatic trials of pain treatments can be improved in areas such as patient recruitment and reporting of methods, analysis, and interpretation of data. These improvements will facilitate translatability to other real-world settings-the purpose of pragmatic trials.
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Meta Analysis
Effectiveness of placebo interventions for patients with non-specific low back pain: a systematic review.
Little is known about the effectiveness of placebo interventions in patients with nonspecific low back pain (LBP). This systematic review assessed the magnitude of the effects of placebo interventions as compared to no intervention in randomized controlled trials (RCTs) including patients with LBP. Embase, MEDLINE (Ovid), and Cochrane CENTRAL databases were searched from inception to December 5, 2019. ⋯ These results show placebo interventions are more effective than no intervention at short-term follow-up in patients with chronic LBP. However, the magnitude of the effects is probably not clinically relevant (approximately 8 points on a 0-100 pain scale). Future research should identify effect modifiers and causal mechanisms explaining the short-term effects of placebo interventions in patients with chronic LBP.
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Pain is a complex experience that involves sensation, emotion, and cognition. The subjectivity of the traditional pain measurement tools has expedited the interest in developing neuroimaging techniques to monitor pain objectively. Among noninvasive neuroimaging techniques, functional near-infrared spectroscopy (fNIRS) has balanced spatial and temporal resolution; yet, it is portable, quiet, and cost-effective. ⋯ Starting from the experimental design, we reviewed the regions of interest, probe localization, data processing, and primary findings of these existing fNIRS studies. We also discussed the fNIRS imaging's potential as a brain surveillance technique for pain, in combination with artificial intelligence and extended reality techniques. We concluded that fNIRS is a brain imaging technique with great potential for objective pain assessment in the clinical environment.
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Poor access to pediatric chronic pain care is a longstanding concern. The COVID-19 pandemic has necessitated virtual care delivery at an unprecedented pace and scale. We conducted a scoping review to create an interactive Evidence and Gap Map of virtual care solutions across a stepped care continuum (ie, from self-directed to specialist care) for youth with chronic pain and their families. ⋯ Evidence and Gap Maps are a novel visual knowledge synthesis tool, which enable rapid evidence-informed decision-making by patients and families, health professionals, and policymakers. This evidence and gap map identified high-quality virtual care solutions for immediate scale and spread and areas with no evidence in need of prioritization. Virtual care should address priorities identified by youth with chronic pain and their families.
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Chronic postsurgical pain (CPSP) affects an estimated 10% to 50% of adults depending on the type of surgical procedure. Clinical prediction models can help clinicians target preventive strategies towards patients at high risk for CPSP. Therefore, the objective of this systematic review was to identify and describe existing prediction models for CPSP in adults. ⋯ The most common predictors identified in final prediction models included preoperative pain in the surgical area, preoperative pain in other areas, age, sex or gender, and acute postsurgical pain. Clinical prediction models may support prevention and management of CPSP, but existing models are at high risk of bias that affects their reliability to inform practice and generalizability to wider populations. Adherence to standardized guidelines for clinical prediction model development is necessary to derive a prediction model of value to clinicians.