☆ 4.3 Article

Rapid reviews methods series: Guidance on team considerations, study selection, data extraction and risk of bias assessment

BMJ EVIDENCE-BASED MEDICINE (2023)

Related references

Note: Only part of the references are listed.

Article Medicine, General & Internal

Rapid Reviews Methods Series: Involving patient and public partners, healthcare providers and policymakers as knowledge users

Chantelle Garritty et al.

Summary: Rapid reviews (RRs) are useful tools for evidence synthesis in urgent healthcare decision-making. However, involving knowledge users (KUs) in RRs, especially patients, is often overlooked. This paper emphasizes the importance of involving KUs, including patients and the public, in RRs to ensure their relevance and applicability for decision-making. It provides guidance on how to involve KUs throughout the review process and presents a case study demonstrating patient and public involvement in developing RRs. Striking a balance between rapidity and meaningful KU involvement is crucial.

BMJ EVIDENCE-BASED MEDICINE (2023)

Add to Collection

Article Medicine, General & Internal

Rapid reviews methods series: Guidance on assessing the certainty of evidence

Gerald Gartlehner et al.

Summary: This paper is a part of a series of methodological guidance provided by the Cochrane Rapid Reviews Methods Group, which explains that rapid reviews (RRs) are designed to speed up the review process while maintaining systematic and transparent methods. The paper focuses on considerations for rating the certainty of evidence (COE) in RRs and provides recommendations for the implementation of GRADE. It also suggests alternative approaches for rating COE if full implementation of GRADE is not feasible.

BMJ EVIDENCE-BASED MEDICINE (2023)

Add to Collection

Article Medicine, General & Internal

Rapid reviews methods series: Guidance on literature search

Irma Klerings et al.

Summary: This paper discusses considerations for conducting rapid reviews (RR), including steps for preparation, planning, information sources, search methods, strategy development, quality assurance, reporting, and record management. To reduce the workload of literature screening, it is recommended to invest time in planning and optimizing the search process, work with an information specialist, select relevant information sources, and use search methods that are likely to identify relevant literature. Quality assurance measures such as peer review and validation of search strategies should be applied to minimize errors.

BMJ EVIDENCE-BASED MEDICINE (2023)

Add to Collection

Review Mathematical & Computational Biology

Crowdsourcing the identification of studies for COVID-19-related Cochrane Rapid Reviews

Anna Noel-Storr et al.

Summary: This study tested whether crowdsourcing could accurately assess the eligibility of studies for reviews under time constraints. The results showed that the crowd completed the screening tasks within 48 hours, with a sensitivity ranging from 94% to 100%, and a crowd consensus ranging from 71% to 92%. The study suggests that crowdsourcing plays a valuable role in rapid evidence syntheses by identifying relevant research.

RESEARCH SYNTHESIS METHODS (2022)

Add to Collection

Article Health Care Sciences & Services

Cochrane Rapid Reviews Methods Group offers evidence-informed guidance to conduct rapid reviews

Chantelle Garritty et al.

Summary: The study aims to develop guidance to support the conduct of rapid reviews, with 26 recommendations presented based on survey results. These recommendations will help synthesize evidence for urgent health issues, but the guidance will need to be updated as new evaluations for some RR methods are conducted due to the current lack of available evidence.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2021)

Add to Collection

Review Health Care Sciences & Services

Systematic review automation tools improve efficiency but lack of knowledge impedes their adoption: a survey

Anna Mae Scott et al.

Summary: The survey found that the majority of respondents have used systematic review automation tools, primarily during the screening stage. They believed these tools saved time and increased accuracy, but lack of knowledge was identified as the main barrier to tool adoption. Respondents suggested the development of new tools for the searching and data extraction stages.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2021)

Add to Collection

Article Nursing

Agreement in Risk of Bias Assessment Between RobotReviewer and Human Reviewers: An Evaluation Study on Randomised Controlled Trials in Nursing-Related Cochrane Reviews

Julian Hirt et al.

Summary: RobotReviewer showed moderate agreement with human reviewers for randomization and allocation concealment in RCTs included in nursing-related Cochrane reviews, with adequate sensitivity for detecting low risk of selection bias. Human reviewers should supervise the semi-automated assessment process.

JOURNAL OF NURSING SCHOLARSHIP (2021)

Add to Collection

Review Medicine, General & Internal

Successful incorporation of single reviewer assessments during systematic review screening: development and validation of sensitivity and work-saved of an algorithm that considers exclusion criteria and count

Nassr Nama et al.

Summary: Targeted application of single-reviewer screening, considering both type and number of exclusion criteria, could retain sensitivity and significantly decrease workload. Further research is needed to explore the potential for combining this approach with crowdsourcing or machine learning methodologies.

SYSTEMATIC REVIEWS (2021)

Add to Collection

Article Health Care Sciences & Services

Creating efficiencies in the extraction of data from randomized trials: a prospective evaluation of a machine learning and text mining tool

Allison Gates et al.

Summary: The study evaluated a machine learning tool for data extraction from randomized trials, finding it performed well in identifying reported data elements and extracting relevant sentences, leading to modest time savings. However, the accuracy of exact matches was low, indicating a need for further improvement.

BMC MEDICAL RESEARCH METHODOLOGY (2021)

Add to Collection

Review Health Care Sciences & Services

Resource use during systematic review production varies widely: a scoping review

B. Nussbaumer-Streit et al.

Summary: The study aimed to investigate the resource use and reasons for resource intensity during systematic review production. Project management and administration, study selection, data extraction, and critical appraisal were found to be the areas with the largest resource use.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2021)

Add to Collection

Article Health Care Sciences & Services

Software tools to support title and abstract screening for systematic reviews in healthcare: an evaluation

Hannah Harrison et al.

BMC MEDICAL RESEARCH METHODOLOGY (2020)

Add to Collection

Article Environmental Sciences

SWIFT-Active Screener: Accelerated document screening through active learning and integrated recall estimation

Brian E. Howard et al.

ENVIRONMENT INTERNATIONAL (2020)

Add to Collection

Article Health Care Sciences & Services

Single-reviewer abstract screening missed 13 percent of relevant studies: a crowd -based, randomized controlled trial

Gerald Gartlehner et al.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2020)

Add to Collection

Article Health Care Sciences & Services

Cochrane Centralised Search Service showed high sensitivity identifying randomized controlled trials: A retrospective analysis

A. H. Noel-Storr et al.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2020)

Add to Collection

Article Health Care Sciences & Services

An evaluation of DistillerSR's machine learning-based prioritization tool for title/abstract screening - impact on reviewer-relevant outcomes

C. Hamel et al.

BMC MEDICAL RESEARCH METHODOLOGY (2020)

Add to Collection

Article Medicine, General & Internal

Machine learning for screening prioritization in systematic reviews: comparative performance of Abstrackr and EPPI-Reviewer

Amy Y. Tsou et al.

SYSTEMATIC REVIEWS (2020)

Add to Collection

Review Health Care Sciences & Services

Few studies exist examining methods for selecting studies, abstracting data, and appraising quality in a systematic review

Reid C. Robson et al.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2019)

Add to Collection

Article Medical Informatics

Machine learning to help researchers evaluate biases in clinical trials: a prospective, randomized user study

Frank Soboczenski et al.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2019)

Add to Collection

Letter Medicine, General & Internal

The Accuracy of Google Translate for Abstracting Data From Non-English-Language Trials for Systematic Reviews

Jeffrey L. Jackson et al.

ANNALS OF INTERNAL MEDICINE (2019)

Add to Collection

Review Health Care Sciences & Services

Single screening versus conventional double screening for study selection in systematic reviews: a methodological systematic review

Siw Waffenschmidt et al.

BMC MEDICAL RESEARCH METHODOLOGY (2019)

Add to Collection

Article Medicine, General & Internal

RoB 2: a revised tool for assessing risk of bias in randomised trials

Jonathan A. C. Sterne et al.

BMJ-BRITISH MEDICAL JOURNAL (2019)

Add to Collection

Article Medicine, General & Internal

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

Robert F. Wolff et al.

ANNALS OF INTERNAL MEDICINE (2019)

Add to Collection

Article Medicine, General & Internal

Assessing the accuracy of machine-assisted abstract screening with DistillerAI: a user study

Gerald Gartlehner et al.

SYSTEMATIC REVIEWS (2019)

Add to Collection

Article Medicine, General & Internal

Performance and usability of machine learning for screening in systematic reviews: a comparative evaluation of three tools

Allison Gates et al.

SYSTEMATIC REVIEWS (2019)

Add to Collection

Article Medicine, General & Internal

AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both

Beverley J. Shea et al.

BMJ-BRITISH MEDICAL JOURNAL (2017)

Add to Collection

Article Medicine, General & Internal

AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both

Beverley J. Shea et al.

BMJ-BRITISH MEDICAL JOURNAL (2017)

Add to Collection

Review Health Care Sciences & Services

Frequency of data extraction errors and methods to increase data extraction quality: a methodological review

Tim Mathes et al.

BMC MEDICAL RESEARCH METHODOLOGY (2017)

Add to Collection

Review Social Sciences, Interdisciplinary

Methods for a rapid systematic review and meta-analysis in evaluating selective serotonin reuptake inhibitors for premature ejaculation

Marrissa Martyn-St James et al.

EVIDENCE & POLICY (2017)

Add to Collection

Review Health Care Sciences & Services

ROBIS: A new tool to assess risk of bias in systematic reviews was developed

Penny Whiting et al.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2016)

Add to Collection

Article Medicine, General & Internal

ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

Jonathan A. C. Sterne et al.

BMJ-BRITISH MEDICAL JOURNAL (2016)

Add to Collection

Article Medicine, General & Internal

ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

Jonathan A. C. Sterne et al.

BMJ-BRITISH MEDICAL JOURNAL (2016)

Add to Collection

Review Mathematical & Computational Biology

Implications of applying methodological shortcuts to expedite systematic reviews: three case studies using systematic reviews from agri-food public health

Mai T. Pham et al.

RESEARCH SYNTHESIS METHODS (2016)

Add to Collection

Review Medicine, General & Internal

Rayyan-a web and mobile app for systematic reviews

Mourad Ouzzani et al.

SYSTEMATIC REVIEWS (2016)

Add to Collection

Review Medicine, General & Internal

A scoping review of rapid review methods

Andrea C. Tricco et al.

BMC MEDICINE (2015)

Add to Collection

Article Medical Laboratory Technology

Interrater reliability: the kappa statistic

Mary L. McHugh

BIOCHEMIA MEDICA (2012)

Add to Collection

Article Health Care Sciences & Services

Single data extraction generated more errors than double data extraction in systematic reviews

Nina Buscemi et al.

JOURNAL OF CLINICAL EPIDEMIOLOGY (2006)

Add to Collection

Article Mathematical & Computational Biology

Identification of randomized controlled trials in systematic reviews: accuracy and reliability of screening records

P Edwards et al.

STATISTICS IN MEDICINE (2002)

Add to Collection

© Peeref 2019-2024. All rights reserved.