Pain
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Understanding how large language model (LLM) recommendations vary with patient race/ethnicity provides insight into how LLMs may counter or compound bias in opioid prescription. Forty real-world patient cases were sourced from the MIMIC-IV Note dataset with chief complaints of abdominal pain, back pain, headache, or musculoskeletal pain and amended to include all combinations of race/ethnicity and sex. Large language models were instructed to provide a subjective pain rating and comprehensive pain management recommendation. ⋯ Race/ethnicity and sex did not influence LLM recommendations. This study suggests that LLMs do not preferentially recommend opioid treatment for one group over another. Given that prior research shows race-based disparities in pain perception and treatment by healthcare providers, LLMs may offer physicians a helpful tool to guide their pain management and ensure equitable treatment across patient groups.
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Chronic pain is common among children and adolescents; however, the diagnoses in the newly developed 11th revision of the International Classification of Diseases (ICD-11) chronic pain chapter are based on adult criteria, overlooking pediatric neurodevelopmental differences. The chronic pain diagnoses have demonstrated good clinical applicability in adults, but to date, no field study has examined these diagnoses to the most specific diagnostic level in a pediatric sample. The current study aimed to explore pediatric representation within the ICD-11, with focus on chronic primary pain. ⋯ The latter also exhibited the lowest agreement between HCPs and algorithm. The current study underscores the need for evidence-based improvements to the ICD-11 diagnostic criteria in pediatrics. Developing pediatric coding notes could improve the visibility of patients internationally and improve the likelihood of receiving reimbursement for necessary treatments through accurate coding.
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Phantom limb pain (PLP) represents a significant challenge after amputation. This study investigated the use of phantom motor execution (PME) and phantom motor imagery (PMI) facilitated by extended reality (XR) for the treatment of PLP. Both treatments used XR, but PME involved overt execution of phantom movements, relying on the decoding of motor intent using machine learning to enable real-time control in XR. ⋯ Pain reduction for PME was larger than previously reported. Despite our initial hypothesis not being confirmed, PME and PMI, aided by XR, are likely to offer meaningful PLP relief to most patients. These findings merit consideration of these therapies as viable treatment options and alternatives to pharmacological treatments.