A Novel Multi-Dimensional Clinical Response Index Dedicated to Improving Global Assessment of Pain in Patients with Persistent Spinal Pain Syndrome after Spinal Surgery, Based on a Real-Life Prospective Multicentric Study (PREDIBACK) and Machine Learning Techniques

The multidimensionality of chronic pain forces us to look beyond isolated assessment such as pain intensity, which does not consider multiple key parameters, particularly in post-operative Persistent Spinal Pain Syndrome (PSPS-T2) patients. Our ambition was to produce a novel Multi-dimensional Clini...

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Auteurs principaux:	Philippe Rigoard, Amine Ounajim, Lisa Goudman, Pierre-Yves Louis, Yousri Slaoui, Manuel Roulaud, Nicolas Naiditch, Bénédicte Bouche, Philippe Page, Bertille Lorgeoux, Sandrine Baron, Elodie Charrier, Laure Poupin, Delphine Rannou, Géraldine Brumauld de Montgazon, Brigitte Roy-Moreau, Nelly Grimaud, Nihel Adjali, Kevin Nivole, Mathilde Many, Romain David, Chantal Wood, Raphael Rigoard, Maarten Moens, Maxime Billot
Format:	article
Langue:	EN
Publié:	MDPI AG 2021
Sujets:	composite score machine learning PSPS failed back surgery syndrome (FBSS) chronic pain pain intensity Medicine R
Accès en ligne:	https://doaj.org/article/7b9d4f30a3394165981db74c74e948e2
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A Novel Multi-Dimensional Clinical Response Index Dedicated to Improving Global Assessment of Pain in Patients with Persistent Spinal Pain Syndrome after Spinal Surgery, Based on a Real-Life Prospective Multicentric Study (PREDIBACK) and Machine Learning Techniques

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