Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods.

Purpose: Accurate prediction of spine surgery outcomes is essential for optimizing treatment strategies. This study presents an enhanced machine learning approach to classify and predict the success of spine surgeries, incorporating advanced oversampling techniques and grid search optimization to improve model performance.

Methods: Various machine learning models, including GaussianNB, ComplementNB, KNN, Decision Tree, KNN with RandomOverSampler, KNN with SMOTE, and grid-searched optimized versions of KNN and Decision Tree, were applied to a dataset of 244 spine surgery patients.

Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: Acomprehensive study.

Objectives: Metabolic bariatric surgery is a critical intervention for patients living with obesity and related health issues. Accurate classification and prediction of patient outcomes are vital for optimizing treatment strategies. This study presents a novel machine learning approach to classify patients in the context of metabolic bariatric surgery, providing insights into the efficacy of different models and variable types.

Analysis of psychometric questionnaires used in patient selection for bariatric surgery.

Introduction: In severe forms of obesity there is a high prevalence of psychopathological comorbidity. Psychiatric evaluation is an important component of comprehensive obesity care and contributes to optimizing therapeutic results after bariatric surgery.

Objective: To assess the effectiveness of psychometric tests used in the protocol for selecting patients for bariatric surgery.

Interleukin 6 and depression in patients affected by Covid-19.

Introduction: Rise of central cytokines resulting from infections produces neuronal changes. Covid-19 allows the study of depressive symptoms in sustained stress and its relationship with molecular mechanisms.

Objectives: To assess correlation between IL-6, IL-1β and TNF-α and depressive symptoms.

Interleukin 6 and depression in patients affected by Covid-19.

Introduction: Rise of central cytokines resulting from infections produces neuronal changes. Covid-19 allows the study of depressive symptoms in sustained stress and its relationship with molecular mechanisms.

Objectives: To assess correlation between IL-6, IL-1β and TNF-α and depressive symptoms.

Evaluation of Dram Score as a Predictor of Poor Postoperative Outcome in Spine Surgery.

Unlabelled: The Distress Risk Assessment Method (DRAM) was presented by Main, Wood and Hillis in 1992 as a simple means of assessing the risk of failure due to psychosocial factors in spine surgery. To our knowledge, it has not been used in our setting. The aim of this study was to analyse the usefulness of the Spanish translation of this instrument to predict poor outcomes.