Scoping reviews, a type of knowledge synthesis, follow a systematic approach to map evidence on a topic and identify main concepts, theories, sources, and knowledge gaps. Although more scoping reviews are being done, their methodological and reporting quality need improvement. This document presents the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist and explanation. The checklist was developed by a 24-member expert panel and 2 research leads following published guidance from the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network. The final checklist contains 20 essential reporting items and 2 optional items. The authors provide a rationale and an example of good reporting for each item. The intent of the PRISMA-ScR is to help readers (including researchers, publishers, commissioners, policymakers, health care providers, guideline developers, and patients or consumers) develop a greater understanding of relevant terminology, core concepts, and key items to report for scoping reviews.

/pdf/prisma-extension-for-scoping-reviews-prisma-scr-checklist-53qupltibg.pdf

PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation

abstracts can be used to eliminate clearly irrelevant reports, obviating the need to obtain the full text of those reports or to return to the bibliographic database at a later time. Accession number / unique identifier: it is advisable to allocate an unused field or fields to store the unique identifier(s) / accession number(s) of records downloaded, such as the PubMed ID number (PMID). This allows subsequent linkage to the full database record and also facilitates information management such as duplicate detection and removal (i.e. de-duplication). Copyright © 2019 The Cochrane Collaboration 75 Affiliation / address: may include the institutional affiliation and / or email address of the author / investigator. Article identifier / digital object identifier (DOI): can be used to cite and link to the full record. Author identifier: can be used to disambiguate authors with similar names. The identifier may be an ORCID (https://orcid.org/about/what-is-orcid/mission?lang=en_US), an International Standard Name Identifier (ISNI) http://www.isni.org/, or from the Virtual International Authority File (VIAF) http://viaf.org/. Clinical trial number: if the record contains a clinical trial number, such as those assigned by the ClinicalTrials.gov or ISRCTN schemes, or a number allocated by the sponsor of the trial, these should be downloaded to aid linking of trial reports to the original studies. In PubMed, the Secondary Source ID field [SI] contains information from secondary sources such as ClinicalTrials.gov and ISRCTN. Similarly, in Ovid MEDLINE, the Secondary Source Linking (SL) field contains the URL to ClinicalTrials.gov and ISRCTN resources where these are mentioned in MEDLINE records. In Embase, the Clinical Trial Number (CN) field contains clinical trial numbers associated with the record. Index terms / thesaurus terms / keywords: These help indicate why records were retrieved if the title and abstract lack detail. Investigator name: this field contains personal names of individuals (e.g. collaborators and investigators) who are not authors of the article but rather are listed in the article as members of a collective / corporate group that is an author of the article. Language: this is the language (or languages) of publication of the original document. Location identifier: this field may also contain a Digital Object Identifier (DOI). Original title: if the original title of the document is not in English and the original title is available, then both titles should be downloaded into separate database fields, to aid correct identification of the reference and de-duplication. See also Transliterated title below. Other term: this field contains largely non-MeSH subject terms (also referred to as Keywords) that describe the content of the article. Author-supplied keywords are included here in PubMed (since 2013). Registry Number / EC Number and Substance Name: these fields provide supplementary subject information regarding substances (chemicals, drugs and enzymes). Transliterated title: in PubMed, this field contains the original title (or, where available, the transliterated title) of each record originally published in a non-English language. This field can be useful for de-duplication. Copyright © 2019 The Cochrane Collaboration 76 Comments, corrections, errata, retractions and updates: It is mandatory, for Cochrane reviews of interventions, to examine any relevant retraction statements and errata for information (MECIR C48). All fields that relate to subsequently published comments, corrections, errata, retractions and updates should be selected for inclusion in the download, so that any impact of these subsequent publications can be taken into account. The MECIR standard specifies: “Care should be taken to ensure that this information is retrieved in all database searches by downloading the appropriate fields, together with the citation data”. For example, the most important fields to consider, in relation to comments, errata etc, together with their field labels in PubMed, are provided in Box 4.a. Box 4.a Important field labels in PubMed in relation to comments, retractions etc CIN: ‘Comment in’ CON: ‘Comment on’ CRI: ‘Corrected and republished in’ CRF: ‘Corrected and republished from’ EIN: ‘Erratum in’ EFR: ‘Erratum for’ ECI: Expression Of Concern In ECF: Expression Of Concern For RIN: ‘Retraction in’ ROF: ‘Retraction of’ RPI: ‘Republished in’ RPF: ‘Republished from’ UIN: ‘Update in’ UOF: ‘Update of’ See: https://www.nlm.nih.gov/bsd/mms/medlineelements.html The above list is provided as an example of the relevant fields in PubMed and as an indicator of the equivalent fields in other databases and service providers. Copyright © 2019 The Cochrane Collaboration 77 4.3 De-duplicating references Because searching to inform systematic reviews is intended to be extensive, thousands of records may be retrieved from multiple sources. References to the same article may be downloaded multiple times from different sources and duplicates can even be found within individual databases. The identification and elimination of duplicate records (de-duplication) reduces unnecessary work during the screening phase. Removing duplicate records from the pool of retrieved references is also necessary if the total number of records identified through database searching (in addition to the total number of additional records identified through other sources) is to be reported correctly in the PRISMA flow diagram together with the total number of records after the duplicates have been removed (Liberati et al 2009). Many Cochrane Information Specialists de-duplicate records so that review authors see only search results that have already been de-duplicated. Formatting of citation information often varies across sources, and automated identification of duplicate references from within reference management software may lead to false positives (removing non-duplicate records) and false negatives (retaining duplicate records). Meanwhile, de-duplication through visual examination of each record is time-consuming and often impractical. Several strategies have been developed to address these issues. Methods for modifying duplicate detection algorithms within reference management software have been developed and tested (Kwon et al 2015, Bramer et al 2016b). An online method to identify search results that are duplicates of PubMed citations has been reported (Sampson et al 2006). Open-source software programs for online duplicate detection have also been developed (Jiang et al 2014, Rathbone et al 2015). There is no consensus on the optimal method for duplicate detection, and the most appropriate method will most likely depend upon the size of the combined dataset, the number and output format of the resources searched, and the skill and comfort level of the operator. A combination of automated methods and visual inspection is often used. After de-duplication of search results, records may be screened for inclusion from within the reference management database. Alternatively, the records may be exported into dedicated screening software or into systematic review production software that includes screening capabilities. If screening is carried out within the reference management database, records for the included and excluded studies can be exported and uploaded into systematic review software such as RevMan. Instructions for importing references into RevMan can be found at: https://community.cochrane.org/help/tools-and-software/revman-5/support-revman5/revman-5-faq. The decision whether to screen within reference management software or within dedicated screening or review production software will most likely depend upon the number of retrieved references, access to various tools and review author preference. Copyright © 2019 The Cochrane Collaboration 78 4.4 Summary points • Cochrane review authors should seek advice from their Cochrane Information Specialist on managing references. • Authors of non-Cochrane reviews should seek advice from their medical / healthcare librarian or information specialist, with experience of managing references for systematic reviews. • Use of reference management software is recommended. • Ensure that all the necessary fields are downloaded. • Remove duplicate references before screening. • Either screen references within the reference management software and export references for the included and excluded studies into systematic review software, or export references to specialized screening software. 5 Supplement information Authors: Carol Lefebvre, Julie Glanville, Simon Briscoe, Anne Littlewood, Chris Marshall, MariaInti Metzendorf, Anna Noel-Storr, Tamara Rader, Farhad Shokraneh, James Thomas and L. Susan Wieland on behalf of the Cochrane Information Retrieval Methods Group

Cochrane handbook for systematic reviews of interventions. Version 5.1.0 [updated March 2011]. The Cochrane Collaboration

Overwhelming evidence shows the quality of reporting of randomised controlled trials (RCTs) is not optimal. Without transparent reporting, readers cannot judge the reliability and validity of trial findings nor extract information for systematic reviews. Recent methodological analyses indicate that inadequate reporting and design are associated with biased estimates of treatment effects. Such systematic error is seriously damaging to RCTs, which are considered the gold standard for evaluating interventions because of their ability to minimise or avoid bias. A group of scientists and editors developed the CONSORT (Consolidated Standards of Reporting Trials) statement to improve the quality of reporting of RCTs. It was first published in 1996 and updated in 2001. The statement consists of a checklist and flow diagram that authors can use for reporting an RCT. Many leading medical journals and major international editorial groups have endorsed the CONSORT statement. The statement facilitates critical appraisal and interpretation of RCTs. During the 2001 CONSORT revision, it became clear that explanation and elaboration of the principles underlying the CONSORT statement would help investigators and others to write or appraise trial reports. A CONSORT explanation and elaboration article was published in 2001 alongside the 2001 version of the CONSORT statement. After an expert meeting in January 2007, the CONSORT statement has been further revised and is published as the CONSORT 2010 Statement. This update improves the wording and clarity of the previous checklist and incorporates recommendations related to topics that have only recently received recognition, such as selective outcome reporting bias. This explanatory and elaboration document-intended to enhance the use, understanding, and dissemination of the CONSORT statement-has also been extensively revised. It presents the meaning and rationale for each new and updated checklist item providing examples of good reporting and, where possible, references to relevant empirical studies. Several examples of flow diagrams are included. The CONSORT 2010 Statement, this revised explanatory and elaboration document, and the associated website (www.consort-statement.org) should be helpful resources to improve reporting of randomised trials.

pdf/consort-2010-explanation-and-elaboration-updated-guidelines-e619t8ofh4.pdf

CONSORT 2010 Explanation and Elaboration: updated guidelines for reporting parallel group randomised trials

The methods and results of systematic reviews should be reported in sufficient detail to allow users to assess the trustworthiness and applicability of the review findings. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement was developed to facilitate transparent and complete reporting of systematic reviews and has been updated (to PRISMA 2020) to reflect recent advances in systematic review methodology and terminology. Here, we present the explanation and elaboration paper for PRISMA 2020, where we explain why reporting of each item is recommended, present bullet points that detail the reporting recommendations, and present examples from published reviews. We hope that changes to the content and structure of PRISMA 2020 will facilitate uptake of the guideline and lead to more transparent, complete, and accurate reporting of systematic reviews.

https://www.bmj.com/content/bmj/372/bmj.n160.full.pdf

PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews.

High quality protocols facilitate proper conduct, reporting, and external review
 of clinical trials. However, the completeness of trial protocols is often
 inadequate. To help improve the content and quality of protocols, an
 international group of stakeholders developed the SPIRIT 2013 Statement
 (Standard Protocol Items: Recommendations for Interventional Trials). The SPIRIT
 Statement provides guidance in the form of a checklist of recommended items to
 include in a clinical trial protocol. This SPIRIT 2013 Explanation and Elaboration paper provides important
 information to promote full understanding of the checklist recommendations. For
 each checklist item, we provide a rationale and detailed description; a model
 example from an actual protocol; and relevant references supporting its
 importance. We strongly recommend that this explanatory paper be used in
 conjunction with the SPIRIT Statement. A website of resources is also available
 (www.spirit-statement.org). The SPIRIT 2013 Explanation and Elaboration paper, together with the Statement,
 should help with the drafting of trial protocols. Complete documentation of key
 trial elements can facilitate transparency and protocol review for the benefit
 of all stakeholders.

https://www.bmj.com/content/bmj/346/bmj.e7586.full.pdf

SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials

PRESS Peer Review of Electronic Search Strategies: 2015 Guideline Statement.

Objective : To examine the sensitivity and precision of Medline searching for randomised clinical trials. Design - Comparison of results of Medline searches to a “gold standard” of known randomised clinical trials in ophthalmology published in 1988; systematic review (meta-analysis) of results of similar, but separate, studies from many fields of medicine. Populations : Randomised clinical trials published in in 1988 in journals indexed in Medline, and those not indexed in Medline and identified by hand search, comprised the gold standard. Gold standards for the other studies combined in the meta-analysis were based on: randomised clinical trials published in any journal, whether indexed in Medline or not; those published in any journal indexed in Medline; or those published in a selected group of journals indexed in Medline. Main outcome measure - Sensitivity (proportion of the total number of known randomised clinical trails identified by the search) and precision (proportion of publications retrieved by Medline that were actually randomised clinical trials) were calculated for each study and combined to obtain weighted means. Searches producing the “best” sensitivity were used for sensitivity and precision estimates when multiple searches were performed. Results : The sensitivity of searching for ophthalmology randomised clinical trials published in 1988 was 82%, when the gold standard was for any journal, 87% for any journal indexed in Medline, and 88% for selected journals indexed in Medline. Weighted means for sensitivity across all studies were 51%, 77%, and 63%, respectively. The weighted mean for precision was 8% (median 32.5%). Most searchers seemed not to use freetext subject terms and truncation of those terms. Conclusion - Although the indexing terms available for searching Medline for randomised clinical trials have improved, sensitivity still remains unsatisfactory. A mechanism is needed to “register” known trials, preferably by retrospective tagging of Medline entries, and incorporating trials published before 1966 and in journals not indexed by Medline into the system.

/pdf/systematic-reviews-identifying-relevant-studies-for-uybwlua30q.pdf

Systematic Reviews: Identifying relevant studies for systematic reviews

Background
Systematic reviewers need to decide how best to reduce bias in identifying studies for their review. Even when journals are indexed in electronic databases, it can still be difficult to identify all relevant studies reported in these journals. Over 1700 journals have been or are being handsearched within The Cochrane Collaboration to identify reports of controlled trials in order to help address these problems.

Objectives
To review systematically empirical studies, which have compared the results of handsearching with the results of searching one or more electronic databases to identify reports of randomized trials.

Search strategy
Studies were sought from The Cochrane Methodology Register (The Cochrane Library, Issue 2, 2002), MEDLINE (1966 to Week 1 July 2002), EMBASE (1980 to Week 25 2002), AMED (1985 to June 2002), BIOSIS (1985 to June 2002), CINAHL (1982 to June 2002), LISA (1969 to July 2002) and PsycINFO (1972 to May 2002). Researchers who may have carried out relevant studies were contacted.

Selection criteria
A research study was considered eligible for this review if it compared handsearching with searching one or more electronic databases to identify reports of randomized trials.

Data collection and analysis
The main outcome measure was the number of reports of randomized trials identified by handsearching as compared to electronic searching. Data were extracted on the electronic database searched, the complexity of electronic search strategy used, the characteristics of the journal reports identified, and the type of trial report identified.

Main results
Thirty-four studies were included. Handsearching identified between 92% to 100% of the total number of reports of randomized trials found in the various comparisons in this review. Searching MEDLINE retrieved 55%, EMBASE 49% and PyscINFO 67%. The retrieval rate of the electronic database varied depending on the complexity of the search. The Cochrane Highly Sensitive Search Strategy (HSSS) identified 80% of the total number of reports of randomized trials found, searches categorised as 'complex' (including the Cochrane HSSS) found 65% and 'simple' found 42%. The retrieval rate for an electronic search was higher when the search was restricted to English language journals; 62% versus 39% for journals published in languages other than English. When the search was restricted to full reports of randomized trials, the retrieval rate for an electronic search improved: a complex search strategy (including the Cochrane HSSS) retrieved 82% of the total number of such reports of randomized trials.

Authors' conclusions
Handsearching still has a valuable role to play in identifying reports of randomized trials for inclusion in systematic reviews of health care interventions, particularly in identifying trials reported as abstracts, letters and those published in languages other than English, together with all reports published in journals not indexed in electronic databases. However, where time and resources are limited, searching an electronic database using a complex search (or the Cochrane HSSS) will identify the majority of trials published as full reports in English language journals, provided, of course, that the relevant journals have been indexed in the database.

Handsearching versus electronic searching to identify reports of randomized trials.

Identifying Randomised Trials

Objectives: To assess the performance of published search filters in finding diagnostic test accuracy studies.



Methods:  Diagnostic test accuracy search filters were identified by searching medline, our own files and by requesting unpublished filters from colleagues. We applied the filters to a case study review of diagnostic test accuracy studies for urinary tract infections (UTI) in young children. The included studies with records in medline formed the gold standard. The performance of the filters in finding those gold standard records was assessed.



Results:  We identified twenty-three diagnostic test accuracy search filters for use with medline. The case study systematic review of UTI included 179 studies of diagnostic test accuracy, of which 160 were available in medline. The filters showed a wide range of sensitivities (range: 20.6% to 86.9%) and precision (range: 1% to 9.4%).



Conclusions:   Our results broadly support those reported in two other studies. The search filters tested do not offer an adequate trade-off between sensitivity and precision to be used to identify studies for systematic reviews. However, there are methods available to explore whether search filters are viable based on an objective statistical analysis of the text and indexing used in records.

Do published search filters to identify diagnostic test accuracy studies perform adequately

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Carol Lefebvre

Author Tools

Chat about Author