Pain
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Decoding pain: uncovering the factors that affect the performance of neuroimaging-based pain models.
Neuroimaging-based pain biomarkers, when combined with machine learning techniques, have demonstrated potential in decoding pain intensity and diagnosing clinical pain conditions. However, a systematic evaluation of how different modeling options affect model performance remains unexplored. This study presents the results from a comprehensive literature survey and benchmark analysis. ⋯ Specifically, incorporating more pain-related brain regions, increasing sample sizes, and averaging less data during training and more data during testing improved performance. These findings offer useful guidance for developing neuroimaging-based biomarkers, underscoring the importance of strategic selection of modeling approaches to build better-performing neuroimaging pain biomarkers. However, the generalizability of these findings to clinical pain requires further investigation.
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Meta Analysis
Is personalization of psychological pain treatments necessary? Evidence from a Bayesian variance ratio meta-analysis.
This is the first study to empirically determine the potential for data-driven personalization in the context of chronic primary pain (CPP). Effect sizes of psychological treatments for individuals with CPP are small to moderate on average. Aiming for better treatment outcomes for the individual patient, the call to personalize CPP treatment increased over time. ⋯ However, this result warrants careful consideration. Further research is needed to shed light on the heterogeneity of psychological treatment studies and thus to uncover the full potential of data-driven personalized psychotherapy for patients with CPP. A Bayesian variance ratio meta-regression indicates empirical evidence that data-driven personalized psychotherapy for patients with chronic primary pain could increase effects of cognitive behavioral therapy.
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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.