'A measurement tool to assess systematic reviews, version 2' (AMSTAR2) is a 16-item tool to critically appraise systematic reviews (SRs) of healthcare interventions. This study aimed to assess the methods and outcomes of AMSTAR2 appraisals in overviews of SRs of interventions for mental and behavioural disorders. The cross-sectional study was conducted using 32 overviews of SRs selected from three electronic databases in January 2021. Data items included overview and SR characteristics and AMSTAR2 appraisal methods and outcomes. Data were extracted by two authors independently and narratively synthesised using descriptive statistics (means ± SD and relative frequencies). SR characteristics were compared based on AMSTAR2 appraisal outcomes using chi-square tests. The 32 overviews appraised SRs of predominantly non-pharmacological interventions for mental disorders. AMSTAR2 appraisals were reported as confidence ratings in 25/32 overviews or individual item scores in 24/32 overviews. Most SRs/overview were non-Cochrane (mean = 94%), included RCTs only (mean = 77%) and were published before AMSTAR2 release (mean = 79%). The confidence ratings derived in 25 overviews for 349 SRs were predominantly critically low (68%). Confidence ratings were similar for SRs with RCTs only versus RCTs+non-RCTs or SRs published before versus after AMSTAR2 release, while Cochrane SRs received more high+moderate than low+critically low confidence ratings (p < 0.01). Confidence ratings derived based on AMSTAR2 do not differentiate among SRs of healthcare interventions except for Cochrane SRs that fulfil the criteria for high confidence ratings. AMSTAR2 items should be consulted to avoid common weaknesses in future SRs.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/jrsm.1532 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Toward structured abdominal examination training using augmented reality.
Int J Comput Assist Radiol Surg
January 2025
Faculty of Computer Science and Research Campus STIMULATE, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany.
Purpose: Structured abdominal examination is an essential part of the medical curriculum and surgical training, requiring a blend of theory and practice from trainees. Current training methods, however, often do not provide adequate engagement, fail to address individual learning needs or do not cover rare diseases.
Methods: In this work, an application for structured Abdominal Examination Training using Augmented Reality (AETAR) is presented.
Knowledge of gross human anatomy among Brazilian physical education students: A cross-sectional study.
Morphologie
January 2025
Laboratório de Anatomia Humana, Instituto de Educação Física e Esportes, Universidade Federal do Ceará, Fortaleza, Brazil; Programa de Pós-Graduação em Ciências Morfofuncionais, Departamento de Morfologia, Faculdade de Medicina, Universidade Federal do Ceará, Fortaleza, Brazil. Electronic address:
Background: Gross human anatomy is essential in undergraduate programs across biological and health sciences. While extensive literature explores medical students' knowledge in this area, studies on non-medical students, particularly those in physical education, are scarce.
Objective: This study assessed the anatomy knowledge among Brazilian physical education students and explored differences based on employment status, type of class instruction (face-to-face vs.
A normative database of Swahili-Chinese paired associates.
Behav Res Methods
January 2025
Institute of Developmental Psychology, Faculty of Psychology, Beijing Normal University, No.19 Xinjiekouwai Street, Haidian District, Beijing, 100875, China.
Over the past few decades, Swahili-English and Lithuanian-English word pair databases have been extensively utilized in research on learning and memory. However, these normative databases are specifically designed for generating study stimuli in learning and memory research involving native (or fluent) English speakers. Consequently, they are not suitable for investigations that encompass populations whose first language is not English, such as Chinese individuals.
View Article and Find Full Text PDFBackground: Early detection and accurate forecasting of AD progression are crucial for timely intervention and management. This study leverages multi-modal data, including MRI scans, brain volumetrics, and clinical notes, utilizing Machine Learning (ML), Deep Learning (DL) and a range of ensemble methods to enhance the forecasting accuracy of Alzheimer's disease.
Method: We utilize the OASIS-3 longitudinal dataset, tracking 1,098 patients over 30 years.
Clinical Manifestations.
Alzheimers Dement
December 2024
University of Wisconsin-Madison, Madison, WI, USA.
Background: Over the past decades, many risk factors for dementia have been identified including sensory and motor functions. Established risk scores to predict onset of cognitive impairment and/or dementia (e.g.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!