Pain
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Meta Analysis
Prognostic factors for adolescent knee pain: an individual participant data meta-analysis of 1281 patients.
Adolescent knee pain has a propensity for chronicity, impacting physical activity and health into adulthood. The aim of this study is to investigate prognostic factors in adolescents with knee pain using individual participant data (IPD) meta-analysis. Studies were identified through a systematic search and a collaborative group. ⋯ Body mass index, pain sensitivity, and knee strength were not associated with prognosis of pain or function. Adolescent knee pain is associated with clinically relevant long-term pain and functional deficits. Self-reported characteristics may help identify those at risk of poor prognosis.
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Meta Analysis
Long-term inflammatory pain does not impact exploratory behavior and stress coping strategies in mice.
Pain puts patients at risk for developing psychiatric conditions such as anxiety and depression. Preclinical mouse models of pain-induced affective behavior vary widely in methodology and results, impairing progress towards improved therapeutics. To systematically investigate the effect of long-term inflammatory pain on exploratory behavior and stress coping strategy, we assessed male C57BL/6J mice in the forced swim test (FST), elevated zero maze, and open field test at 4 and 6 weeks postinjection of Complete Freund's Adjuvant, while controlling for testing order and combination. ⋯ A meta-analysis of similar studies indicated a modest, significant effect of Complete Freund's Adjuvant on exploratory behavior, but not immobility in the FST, and high heterogeneity among effect sizes in all 3 paradigms. Given the urgency for understanding the mechanisms of pain comorbidities and identifying novel therapies, these findings support the reallocation of our limited resources away from such unreliable assays and toward motivated and naturalistic behaviors. Future studies in pain and psychiatric translational research may benefit by considering outcomes beyond binary categorization, quantifying the associations between multiple measured behaviors, and agnostically identifying subtle yet meaningful patterns in behaviors.