The journal of pain : official journal of the American Pain Society
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Group-delivered programs for chronic pain are evidence-based and frequently used. The contribution of group factors to outcomes is unclear and there are no integrated findings on consumer perceptions of the group itself in programs for people with chronic pain. The aim of this systematic review was to search and synthesise qualitative data specifically related to the group itself in studies investigating group-delivered programs for people with chronic pain (PROSPERO, CRD42023382447). ⋯ PERSPECTIVE: This review demonstrates that many consumers valued peer interaction and used comparison-based cognitive processing within group-delivered programs for chronic pain. Dialogue-based interactions with similar others promoted cognitive, affect and behaviour changes. Group factors may have been underestimated and outcomes could be influenced if peer interactions within programs were optimised.
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The use of electronic health records (EHR) for chronic pain phenotyping has gained significant attention in recent years, with various algorithms being developed to enhance accuracy. Structured data fields (e.g., pain intensity, treatment modalities, diagnosis codes, and interventions) offer standardized templates for capturing specific chronic pain phenotypes. This study aims to determine which chronic pain case definitions derived from structured data elements achieve the best accuracy, and how these validation metrics vary by sociodemographic and disease-related factors. ⋯ While our current algorithms provide valuable insights, enhancement is needed to ensure more reliable chronic pain identification across diverse patient populations. PERSPECTIVES: This study evaluates chronic pain phenotyping algorithms using electronic health records, highlighting variability in performance across sociodemographic and disease-related factors. By combining structured data elements, the findings advance chronic pain identification, promoting equitable healthcare practices and highlighting the need for tailored algorithms to address subgroup-specific biases and improve outcomes.