Public articles linked to the same research event.
Pain management In this prospective cohort study, 96 patients with spinal pain completed an extensive questionnaire covering pain, mood, sleep, lifestyle, and treatment expectations at baseline and at 1 and 3 months, with binary recovery outcomes defined at 3 months for pain intensity, disability, quality of life, and Patient Global Impression of Change; a three-step machine learning framework combining SHAP-based candidate feature selection, Leave-One-Out Cross-Validation with permutation testing, and generalization testing yielded AUC values of 0.93-0.99 in LOOCV and 0.62-0.
In this prospective cohort study, 96 patients with spinal pain completed an extensive questionnaire covering pain, mood, sleep, lifestyle, and treatment expectations at baseline and at 1 and 3 months, with binary recovery outcomes defined at 3 months for pain intensity, disability, quality of life, and Patient Global Impression of Change; a three-step machine learning framework combining SHAP-based candidate feature selection, Leave-One-Out Cross-Validation with permutation testing, and generalization testing yielded AUC values of 0.93-0.99 in LOOCV and 0.62-0.
In this prospective cohort study, 96 patients with spinal pain completed an extensive questionnaire covering pain, mood, sleep, lifestyle, and treatment expectations at baseline and at 1 and 3 months, with binary recovery outcomes defined at 3 months for pain intensity, disability, quality of life, and Patient Global Impression of Change; a three-step machine learning framework combining SHAP-based candidate feature selection, Leave-One-Out Cross-Validation with permutation testing, and generalization testing yielded AUC values of 0.93-0.99 in LOOCV and 0.62-0.
In this prospective cohort study, 96 patients with spinal pain completed an extensive questionnaire covering pain, mood, sleep, lifestyle, and treatment expectations at baseline and at 1 and 3 months, with binary recovery outcomes defined at 3 months for pain intensity, disability, quality of life, and Patient Global Impression of Change; a three-step machine learning framework combining SHAP-based candidate feature selection, Leave-One-Out Cross-Validation with permutation testing, and generalization testing yielded AUC values of 0.93-0.99 in LOOCV and 0.62-0.