Metaforest Algorithm Insights: Predictors of Nocebo Response in ADHD

Mireia Porta, Maggie Barcheni,David Ramírez-saco,Ruth Cunill, Magí Farré,Marc Saez,Beatriz López,Xavier Castells

Current Psychopharmacology（2024）

TransLab Research Group Department of Medical Sciences Universitat de Girona | Parc Sanitari Sant Joan de Déu-Numància | Research Group on Statistics

Cited 0|Views2

Abstract

Background: Predicting the nocebo response in randomized controlled trials (RCTs) is crucial as it can help minimize its influence and improve the evaluation of the side effects of interventions for ADHD. The aim of this study is to determine the effect of covariates related to study design, intervention, and patients’ characteristics on the nocebo response in patients with Attention Deficit Hyperactivity Disorder (ADHD) using Metaforest, and, ultimately, to investigate Metaforest’s performance in predicting nocebo response in ADHD RCTs. Methods: This study is a secondary analysis of a previously published systematic review [1]. Nocebo response was defined as the proportion of patients experiencing at least one AE while receiving a placebo. We used Metaforest for investigating patient-, intervention, and study design-related nocebo response moderators in ADHD RCTs. Results: One hundred and five studies were included. Overall, 55.4% of patients experienced at least one AE while receiving placebo. However, between-study variability on nocebo response was very high, with nocebo response ranging from 4.2% to 90.2%, leading to high statistical heterogeneity (I2 = 88.3%). Older patients showed a higher nocebo response. The moderating effects of the year of publication, treatment length and gender were also significant. The predictive performance of the model was low-moderate (R2 test = 0, 1922; MSE = 0, 0408). Conclusion: Age was the most important nocebo response modifier, followed by year of publication, treatment length and gender. Metaforest lacked the capability to predict nocebo responses in future studies.

Translated text

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper

Data Disclaimer

The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn

Chat Paper

【要点】：本研究使用Metaforest算法探讨ADHD患者随机对照试验中影响安慰剂效应（nocebo response）的协变量，发现年龄是最重要的调节因子，但Metaforest算法预测未来安慰剂效应的能力有限。

【方法】：通过二次分析已发表的系统性回顾，利用Metaforest算法分析患者特征、干预措施和试验设计对安慰剂效应的影响。

【实验】：研究纳入105项研究，结果显示55.4%的患者在服用安慰剂时至少出现了一种不良事件（AE），但各研究间的安慰剂效应差异很大（4.2%至90.2%），模型预测性能为低-中等（R2 test = 0.1922; MSE = 0.0408）。