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

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...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: 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
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
composite score
machine learning
PSPS
failed back surgery syndrome (FBSS)
chronic pain
pain intensity
Medicine
R
Acceso en línea:https://doaj.org/article/7b9d4f30a3394165981db74c74e948e2
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Ejemplares similares