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Competing Interests: Declaration of Competing Interest All authors are member of the GRADE Guidance Group. HJS and GG are cochairs of GRADE.

An evidence-based approach is considered the gold standard for health decision-making. Sometimes, a guideline panel might judge the certainty that the desirable effects of an intervention clearly outweigh its undesirable effects as high, but the body of supportive evidence is indirect. In such cases, the application of the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach for grading the strength of recommendations is inappropriate. Instead, the GRADE Working Group has recommended developing ungraded best or good practice statement (GPS) and developed guidance under which circumsances they would be appropriate.Through an evaluation of COVID-1- related recommendations on the eCOVID Recommendation Map (COVID-19.recmap.org), we found that recommendations qualifying a GPS were widespread. However, guideline developers failed to label them as GPS or transparently report justifications for their development. We identified ways to improve and facilitate the operationalisation and implementation of the GRADE guidance for GPS.Herein, we propose a structured process for the development of GPSs that includes applying a sequential order for the GRADE guidance for developing GPS. This operationalisation considers relevant evidence-to-decision criteria when assessing the net consequences of implementing the statement, and reporting information supporting judgments for each criterion. We also propose a standardised table to facilitate the identification of GPS and reporting of their development. This operationalised guidance, if endorsed by guideline developers, may palliate some of the shortcomings identified. Our proposal may also inform future updates of the GRADE guidance for GPS., Competing Interests: Competing interests: This work was supported by grants from Canadian Institutes of Health (FRN VR4-172741 and GA3-177732) and WHO during the conduct of the study. EA, HJS and PA-C report contribution to the development of the original five criteria for assessing the appropriateness of issuing good practice statements. The remaining authors have nothing else to declare., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Various formalisms have been developed to represent clinical practice guideline recommendations in a computer-interpretable way. However, none of the existing formalisms leverage the structured and computable information that emerge from the evidence-based guideline development process. Thus, we here propose a FHIR-based format that uses computer-interpretable representations of the knowledge artifacts that emerge during the process of evidence-based guideline development to directly serve as the basis of evidence-based recommendations., Methods: We identified the information required to represent evidence-based clinical practice guideline recommendations and reviewed the knowledge artifacts emerging during the evidence-based guideline development process. We then conducted a consensus-based design process with domain experts to develop an information model for guideline recommendation representation that is structurally aligned to the evidence-based guideline recommendation development process and a corresponding representation based on FHIR resources developed for evidence-based medicine (EBMonFHIR). The resulting recommendations were modelled and represented in conformance with the FHIR Clinical Guidelines (CPG-on-FHIR) implementation guide., Results: The information model of evidence-based clinical guideline recommendations and its EBMonFHIR-/CPG-on-FHIR-based representation contain the clinical contents of individual guideline recommendations, a set of metadata for the recommendations, the ratings for the recommendations (e.g., strength of recommendation, certainty of overall evidence), the ratings of certainty of evidence for individual outcomes (e.g., risk of bias) and links to the underlying evidence (systematic reviews based on primary studies). We created profiles and an implementation guide for all FHIR resources required to represent the knowledge artifacts generated during evidence-based guideline development and their re-use as the basis for recommendations and used the profiles to implement an exemplary clinical guideline recommendation., Conclusions: The FHIR implementation guide presented here can be used to directly link the evidence assessment process of evidence-based guideline recommendation development, i.e. systematic reviews and evidence grading, and the underlying evidence from primary studies to the resulting guideline recommendations. This not only allows the evidence on which recommendations are based on to be evaluated transparently and critically, but also enables guideline developers to leverage computable evidence in a more direct way to facilitate the generation of computer-interpretable guideline recommendations., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Brian S. Alper is the project lead for the EBMonFHIR project and owns Computable Publishing LLC which produces and hosts the FEvIR Platform. Otherwise, the authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: Timely publication of clinical trial results is central for evidence-based medicine. In this follow-up study we benchmark the performance of German university medical centers (UMCs) regarding timely dissemination of clinical trial results in recent years., Methods: Following the same search and tracking methods used in our previous study for the years 2009 - 2013, we identified trials led by German UMCs completed between 2014 and 2017 and tracked results dissemination for the identified trials., Results: We identified 1,658 trials in the 2014 -2017 cohort. Of these trials, 43% published results as either journal publication or summary results within 24 months after completion date, which is an improvement of 3.8% percentage points compared to the previous study. At the UMC level, the proportion published after 24 months ranged from 14% to 71%. Five years after completion, 30% of the trials still remained unpublished., Conclusion: Despite minor improvements compared to the previously investigated cohort, the proportion of timely reported trials led by German UMCs remains low. German UMCs should take further steps to improve the proportion of timely reported trials., (Copyright © 2021. Published by Elsevier Inc.)

Background: Although quality indicators are frequently derived from guidelines, there is a substantial gap in collaboration between the corresponding parties. To optimise workflow, guideline recommendations and quality assurance should be aligned methodologically and practically. Learning from the European Commission Initiative on Breast Cancer (ECIBC), our objective was to bring the key knowledge and most important considerations from both worlds together to inform European Commission future initiatives., Methods: We undertook several steps to address the problem. First, we conducted a feasibility study that included a survey, interviews and a review of manuals for an integrated guideline and quality assurance (QA) scheme that would support the European Commission. The feasibility study drew from an assessment of the ECIBC experience that followed commonly applied strategies leading to separation of the guideline and QA development processes. Secondly, we used results of a systematic review to inform our understanding of methodologies for integrating guideline and QA development. We then, in a third step, used the findings to prepare an evidence brief and identify key aspects of a methodological framework for integrating guidelines QA through meetings with key informants., Results: Seven key themes emerged to be taken into account for integrating guidelines and QA schemes: (1) evidence-based integrated guideline and QA frameworks are possible, (2) transparency is key in clearly documenting the source and rationale for quality indicators, (3) intellectual and financial interests should be declared and managed appropriately, (4) selection processes and criteria for quality indicators need further refinement, (5) clear guidance on retirement of quality indicators should be included, (6) risks of an integrated guideline and QA Group can be mitigated, and (7) an extension of the GIN-McMaster Guideline Development Checklist should incorporate QA considerations., Discussion: We concluded that the work of guideline and QA developers can be integrated under a common methodological framework and we provided key findings and recommendations. These two worlds, that are fundamental to improving health, can both benefit from integration.

Objectives: Health guidelines are a key knowledge translation tool produced and used by numerous stakeholders worldwide. Effective participation in guideline development groups or development groups is crucial for guideline success, yet little guidance exists for members of these groups. In this study, we present the Guideline Participant Tool (GPT) to support effective participation in guideline groups, in particular those using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach., Study Design and Setting: We used a mixed methods and iterative approach to develop a tool to support guideline participation. We used the findings of a published systematic review to develop an initial list of items for considerations for guideline participants. Then, we refined this list through key informant interviews with guideline chairs, sponsors, and participants. Finally, we validated the GPT in three guideline groups with 26 guideline group members., Results: The initial list of items based on 37 articles from the existing systematic review included 15 themes and 61 items for a draft tool. Ten key informant interviews helped us refine the list to include the following themes: selection of participants, guideline group process, and tool format. 26 respondents completed the validation survey from three guideline groups. Refinement of the tool ultimately generated a GPT with 33 items for participant consideration before, during, and in follow-up to guideline group meetings., Conclusion: The GPT contains helpful guidance for all guideline participants, particularly those without previous guideline experience. Future research should further explore the need for additional tools to support guideline participants and identify and develop strategies for improving guideline members' participation in guideline groups. This work will be incorporated into INGUIDE.org guideline training and credentialing efforts by the Guidelines International Network and McMaster University., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: The Grading of Recommendations Assessment, Development, and Evaluations (GRADE) system for assessing certainty in a body of evidence currently uses two levels, serious and very serious, for downgrading on a single domain. In the context of newer risk of bias instruments, such as Risk of Bias in Non-Randomized Studies I (ROBINS-I), evidence generated by nonrandomized studies may justify rating down by more than two levels on a single domain. Given the importance users of GRADE assign to terminology, our objective was to assess what term GRADE stakeholders would prefer for rating down certainty by three levels., Study Design and Setting: We conducted a purposefully sampled online survey of GRADE stakeholders to assess possible terms including "critically serious," "extremely serious," "most serious," and "very, very serious" and conducted a descriptive and thematic analysis of responses. We then facilitated a GRADE working group workshop to generate consensus., Results: A total of 225 respondents ranked and rated "extremely serious" highest, closely followed by "critically serious." Respondents felt that "extremely serious" was "more understandable" and "easiest to interpret". GRADE working group members described that the terms "extremely serious" appeared clearer and easier to translate in other languages., Conclusion: Based on this stakeholder-driven study, "extremely serious" is the preferred term to rate down certainty of evidence by three levels in the GRADE approach., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology is used to assess and report certainty of evidence and strength of recommendations. This GRADE concept article is not GRADE guidance but introduces certainty of net benefit, defined as the certainty that the balance between desirable and undesirable health effects is favourable. Determining certainty of net benefit requires considering certainty of effect estimates, the expected importance of outcomes and variability in importance, and the interaction of these concepts. Certainty of net harm is the certainty that the net effect is unfavourable. Guideline panels using or testing this approach might limit strong recommendations to actions with a high certainty of net benefit or against actions with a moderate or high certainty of net harm. Recommendations may differ in direction or strength from that suggested by the certainty of net benefit or harm when influenced by cost, equity, acceptability or feasibility., Competing Interests: Competing interests: All authors are members of the GRADE Working Group and conduct scholarly activity or professional services related to the concepts in this article. BSA and PO are employed by EBSCO Information Services and IK is employed by Duodecim Medical Publications Ltd., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Rare diseases are a global public health priority. Though each disease is rare, when taken together the thousands of known rare diseases cause significant morbidity and mortality, impact quality of life, and confer a social and economic burden on families and communities. These conditions are, by their nature, encountered very infrequently by individual clinicians, who may feel unprepared to address their diagnosis and treatment. Clinical practice guidelines are necessary to support clinical and policy decisions. However, creating guidelines for rare diseases presents specific challenges, including a paucity of high certainty evidence to inform panel recommendations., Methods: This paper draws from the authors' experience in the development of clinical practice guidelines for three rare diseases: hemophilia, sickle cell disease, and catastrophic antiphospholipid syndrome., Results: We have summarized a number of strategies for eliciting and synthesizing evidence that are compatible with the rigorous, internationally accepted standards for guideline development set out by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. These strategies include: use of pre-existing and ad hoc qualitative research, use of systematic observation forms, use of registry data, and thoughtful use of indirect evidence. Their use in three real guideline development efforts, as well as their theoretical underpinnings, are discussed. Avenues for future research to improve clinical practice guideline creation for rare diseases - and any disease affected by a relative lack of evidence - are also identified., Conclusions: Rigorous clinical practice guidelines are needed to improve the care of the millions of people worldwide who suffer from rare diseases. Innovative evidence elicitation and synthesis methods will benefit not only the rare disease community, but also individuals with common diseases who have rare presentations, suffer rare complications, or require nascent therapies. Further refinement and improved uptake of these innovative methods should lead to higher quality clinical practice guidelines in rare diseases.

Background: The GRADE-CERQual (Confidence in Evidence from Reviews of Qualitative research) approach has been developed by the GRADE (Grading of Recommendations Assessment, Development and Evaluation) Working Group. The approach has been developed to support the use of findings from qualitative evidence syntheses in decision-making, including guideline development and policy formulation. CERQual includes four components for assessing how much confidence to place in findings from reviews of qualitative research (also referred to as qualitative evidence syntheses): (1) methodological limitations, (2) coherence, (3) adequacy of data and (4) relevance. This paper is part of a series providing guidance on how to apply CERQual and focuses on a probable fifth component, dissemination bias. Given its exploratory nature, we are not yet able to provide guidance on applying this potential component of the CERQual approach. Instead, we focus on how dissemination bias might be conceptualised in the context of qualitative research and the potential impact dissemination bias might have on an overall assessment of confidence in a review finding. We also set out a proposed research agenda in this area., Methods: We developed this paper by gathering feedback from relevant research communities, searching MEDLINE and Web of Science to identify and characterise the existing literature discussing or assessing dissemination bias in qualitative research and its wider implications, developing consensus through project group meetings, and conducting an online survey of the extent, awareness and perceptions of dissemination bias in qualitative research., Results: We have defined dissemination bias in qualitative research as a systematic distortion of the phenomenon of interest due to selective dissemination of studies or individual study findings. Dissemination bias is important for qualitative evidence syntheses as the selective dissemination of qualitative studies and/or study findings may distort our understanding of the phenomena that these syntheses aim to explore and thereby undermine our confidence in these findings. Dissemination bias has been extensively examined in the context of randomised controlled trials and systematic reviews of such studies. The effects of potential dissemination bias are formally considered, as publication bias, within the GRADE approach. However, the issue has received almost no attention in the context of qualitative research. Because of very limited understanding of dissemination bias and its potential impact on review findings in the context of qualitative evidence syntheses, this component is currently not included in the GRADE-CERQual approach., Conclusions: Further research is needed to establish the extent and impacts of dissemination bias in qualitative research and the extent to which dissemination bias needs to be taken into account when we assess how much confidence we have in findings from qualitative evidence syntheses.

Decisions in public health should be based on the best available evidence, reviewed and appraised using a rigorous and transparent methodology. The Project on a Framework for Rating Evidence in Public Health (PRECEPT) defined a methodology for evaluating and grading evidence in infectious disease epidemiology, prevention and control that takes different domains and question types into consideration. The methodology rates evidence in four domains: disease burden, risk factors, diagnostics and intervention. The framework guiding it has four steps going from overarching questions to an evidence statement. In step 1, approaches for identifying relevant key areas and developing specific questions to guide systematic evidence searches are described. In step 2, methodological guidance for conducting systematic reviews is provided; 15 study quality appraisal tools are proposed and an algorithm is given for matching a given study design with a tool. In step 3, a standardised evidence-grading scheme using the Grading of Recommendations Assessment, Development and Evaluation Working Group (GRADE) methodology is provided, whereby findings are documented in evidence profiles. Step 4 consists of preparing a narrative evidence summary. Users of this framework should be able to evaluate and grade scientific evidence from the four domains in a transparent and reproducible way.

Background: There are diverse opinions and confusion about defining and including patient values and preferences (i.e. the importance people place on the health outcomes) in the guideline development processes. This article aims to provide an overview of a process for systematically incorporating values and preferences in guideline development., Methods: In 2013 and 2014, we followed the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to adopt, adapt and develop 226 recommendations in 22 guidelines for the Ministry of Health of the Kingdom of Saudi Arabia. To collect context-specific values and preferences for each recommendation, we performed systematic reviews, asked clinical experts to provide feedback according to their clinical experience, and consulted patient representatives., Results: We found several types of studies addressing the importance of outcomes, including those reporting utilities, non-utility measures of health states based on structured questionnaires or scales, and qualitative studies. Guideline panels used the relative importance of outcomes based on values and preferences to weigh the balance of desirable and undesirable consequences of alternative intervention options. However, we found few studies addressing local values and preferences., Conclusions: Currently there are different but no firmly established processes for integrating patient values and preferences in healthcare decision-making of practice guideline development. With GRADE Evidence-to-Decision (EtD) frameworks, we provide an empirical strategy to find and incorporate values and preferences in guidelines by performing systematic reviews and eliciting information from guideline panel members and patient representatives. However, more research and practical guidance are needed on how to search for relevant studies and grey literature, assess the certainty of this evidence, and best summarize and present the findings.

Objectives: To describe the grading of recommendations assessment, development and evaluation (GRADE) interactive evidence to decision (EtD) frameworks for tests and test strategies for clinical, public health, or coverage decisions., Study Design and Setting: As part of the GRADE Working Group's DECIDE project, we conducted workshops, user testing with systematic review authors, guideline developers and other decision makers, and piloted versions of the EtD framework., Results: EtD frameworks for tests share the structure, explicitness, and transparency of other EtD frameworks. They require specifying the purpose of the test, linked or related management, and the key outcomes of concern for different test results and subsequent management. The EtD criteria address test accuracy and assessments of the certainty of the additional evidence necessary for decision making. When there is no direct evidence of test effects on patient-important outcomes, formal or informal modeling is needed to estimate effects. We describe the EtD criteria based on examples developed with GRADEpro (www.gradepro.org), GRADE's software that also allows development and dissemination of interactive summary of findings tables., Conclusion: EtD frameworks for developing recommendations and making decisions about tests lay out the sequential steps in reviewing and assessing the different types of evidence that need to be linked., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: Systematic reviews represent one of the most important tools for knowledge translation but users often struggle with understanding and interpreting their results. GRADE Summary-of-Findings tables have been developed to display results of systematic reviews in a concise and transparent manner. The current format of the Summary-of-Findings tables for presenting risks and quality of evidence improves understanding and assists users with finding key information from the systematic review. However, it has been suggested that additional methods to present risks and display results in the Summary-of-Findings tables are needed., Methods/design: We will conduct a non-inferiority parallel-armed randomized controlled trial to determine whether an alternative format to present risks and display Summary-of-Findings tables is not inferior compared to the current standard format. We will measure participant understanding, accessibility of the information, satisfaction, and preference for both formats. We will invite systematic review users to participate (that is clinicians, guideline developers, and researchers). The data collection process will be undertaken using the online 'Survey Monkey' system. For the primary outcome understanding, non-inferiority of the alternative format (Table A) to the current standard format (Table C) of Summary-of-Findings tables will be claimed if the upper limit of a 1-sided 95% confidence interval (for the difference of proportion of participants answering correctly a given question) excluded a difference in favor of the current format of more than 10%., Discussion: This study represents an effort to provide systematic reviewers with additional options to display review results using Summary-of-Findings tables. In this way, review authors will have a variety of methods to present risks and more flexibility to choose the most appropriate table features to display (that is optional columns, risks expressions, complementary methods to display continuous outcomes, and so on)., Trials Registration: NCT02022631 (21 December 2013).

Background: The internet is an easy and always accessible source of information for cancer patients. The aim of our study was to evaluate the information provided on German websites., Material and Methods: We developed an instrument based on criteria for patient information from the German Network for Evidence-based Medicine, the Agency for Quality in Medicine, HONcode, DISCERN, and the afgis. We simulated a patient's search and derived the websites for evaluation. We analyzed the visibility of each website and evaluated the websites using the developed instrument., Results: We analyzed 77 websites. The highest visibility index was shown by 4 profit websites. Websites from professional societies and self-help groups have low rankings. Concerning quality, websites from non-profit providers and self-help groups are on top. Websites with a profit interest have the lowest average score., Conclusions: A discrepancy exists between the visibility and the quality of the analyzed websites. With the internet becoming an important source of information on cancer treatments for patients, this may lead to false information and wrong decisions. We provide a list of suggestions as to how this risk may be reduced by complementary information from the physician and from trustworthy websites., (© 2015 S. Karger GmbH, Freiburg.)

Background: The Project on a Framework for Rating Evidence in Public Health (PRECEPT) was initiated and is being funded by the European Centre for Disease Prevention and Control (ECDC) to define a methodology for evaluating and grading evidence and strength of recommendations in the field of public health, with emphasis on infectious disease epidemiology, prevention and control. One of the first steps was to review existing quality appraisal tools (QATs) for individual research studies of various designs relevant to this area, using a question-based approach., Methods: Through team discussions and expert consultations, we identified 20 relevant types of public health questions, which were grouped into six domains, i.e. characteristics of the pathogen, burden of disease, diagnosis, risk factors, intervention, and implementation of intervention. Previously published systematic reviews were used and supplemented by expert consultation to identify suitable QATs. Finally, a matrix was constructed for matching questions to study designs suitable to address them and respective QATs. Key features of each of the included QATs were then analyzed, in particular in respect to its intended use, types of questions and answers, presence/absence of a quality score, and if a validation was performed., Results: In total we identified 21 QATs and 26 study designs, and matched them. Four QATs were suitable for experimental quantitative study designs, eleven for observational quantitative studies, two for qualitative studies, three for economic studies, one for diagnostic test accuracy studies, and one for animal studies. Included QATs consisted of six to 28 items. Six of the QATs had a summary quality score. Fourteen QATs had undergone at least one validation procedure., Conclusions: The results of this methodological study can be used as an inventory of potentially relevant questions, appropriate study designs and QATs for researchers and authorities engaged with evidence-based decision-making in infectious disease epidemiology, prevention and control.

In the GRADE approach, the strength of a recommendation reflects the extent to which we can be confident that the composite desirable effects of a management strategy outweigh the composite undesirable effects. This article addresses GRADE's approach to determining the direction and strength of a recommendation. The GRADE describes the balance of desirable and undesirable outcomes of interest among alternative management strategies depending on four domains, namely estimates of effect for desirable and undesirable outcomes of interest, confidence in the estimates of effect, estimates of values and preferences, and resource use. Ultimately, guideline panels must use judgment in integrating these factors to make a strong or weak recommendation for or against an intervention., (Copyright © 2014. Published by Elsevier GmbH.)

Unlabelled: Presenting continuous outcomes in Summary of Findings tables presents particular challenges to interpretation. When each study uses the same outcome measure, and the units of that measure are intuitively interpretable (e.g., duration of hospitalisation, duration of symptoms), presenting differences in means is usually desirable. When the natural units of the outcome measure are not easily interpretable, choosing a threshold to create a binary outcome and presenting relative and absolute effects become a more attractive alternative. When studies use different measures of the same construct, calculating summary measures requires converting to the same units of measurement for each study. The longest standing and most widely used approach is to divide the difference in means in each study by its standard deviation and present pooled results in standard deviation units (standardised mean difference). Disadvantages of this approach include vulnerability to varying degrees of heterogeneity in the underlying populations and difficulties in interpretation. Alternatives include presenting results in the units of the most popular or interpretable measure, converting to dichotomous measures and presenting relative and absolute effects, presenting the ratio of the means of intervention and control groups, and presenting the results in minimally important difference units. We outline the merits and limitations of each alternative and provide guidance for meta-analysts and guideline developers., Key Points: Summary of Findings tables provide succinct presentations of evidence quality and magnitude of effects. Summarising the findings of continuous outcomes presents special challenges to interpretation that become daunting when individual trials use different measures for the same construct. The most commonly used approach to providing pooled estimates for different measures, presenting results in standard deviation units, has limitations related to both statistical properties and interpretability. Potentially preferable alternatives include presenting results in the natural units of the most popular measure, transforming into a binary outcome and presenting relative and absolute effects, presenting the ratio of the means of intervention and control groups, and presenting results in preestablished minimally important difference units., (Copyright © 2014. Published by Elsevier GmbH.)

This article describes the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to classifying the direction and strength of recommendations. The strength of a recommendation, separated into strong and weak, is defined as the extent to which one can be confident that the desirable effects of an intervention outweigh its undesirable effects. Alternative terms for a weak recommendation include conditional, discretionary, or qualified. The strength of a recommendation has specific implications for patients, the public, clinicians, and policy makers. Occasionally, guideline developers may choose to make "only-in-research" recommendations. Although panels may choose not to make recommendations, this choice leaves those looking for answers from guidelines without the guidance they are seeking. GRADE therefore encourages panels to, wherever possible, offer recommendations., (Copyright © 2014. Published by Elsevier GmbH.)

Background: Randomized control trials (RCTs) stopped early for benefit (truncated RCTs) are increasingly common and, on average, overestimate the relative magnitude of benefit by approximately 30%. Investigators stop trials early when they consider it is no longer ethical to enroll patients in a control group. The goal of this systematic review is to determine how investigators of ongoing or planned RCTs respond to the publication of a truncated RCT addressing a similar question., Methods/design: We will conduct systematic reviews to update the searches of 210 truncated RCTs to identify similar trials ongoing at the time of publication, or started subsequently, to the truncated trials ('subsequent RCTs'). Reviewers will determine in duplicate the similarity between the truncated and subsequent trials. We will analyze the epidemiology, distribution, and predictors of subsequent RCTs. We will also contact authors of subsequent trials to determine reasons for beginning, continuing, or prematurely discontinuing their own trials, and the extent to which they rely on the estimates from truncated trials., Discussion: To the extent that investigators begin or continue subsequent trials they implicitly disagree with the decision to stop the truncated RCT because of an ethical mandate to administer the experimental treatment. The results of this study will help guide future decisions about when to stop RCTs early for benefit.

In the GRADE approach, the strength of a recommendation reflects the extent to which we can be confident that the composite desirable effects of a management strategy outweigh the composite undesirable effects. This article addresses GRADE's approach to determining the direction and strength of a recommendation. The GRADE describes the balance of desirable and undesirable outcomes of interest among alternative management strategies depending on four domains, namely estimates of effect for desirable and undesirable outcomes of interest, confidence in the estimates of effect, estimates of values and preferences, and resource use. Ultimately, guideline panels must use judgment in integrating these factors to make a strong or weak recommendation for or against an intervention., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Objective: We evaluated the inter-rater reliability (IRR) of assessing the quality of evidence (QoE) using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach., Study Design and Setting: On completing two training exercises, participants worked independently as individual raters to assess the QoE of 16 outcomes. After recording their initial impression using a global rating, raters graded the QoE following the GRADE approach. Subsequently, randomly paired raters submitted a consensus rating., Results: The IRR without using the GRADE approach for two individual raters was 0.31 (95% confidence interval [95% CI] = 0.21-0.42) among Health Research Methodology students (n = 10) and 0.27 (95% CI = 0.19-0.37) among the GRADE working group members (n = 15). The corresponding IRR of the GRADE approach in assessing the QoE was significantly higher, that is, 0.66 (95% CI = 0.56-0.75) and 0.72 (95% CI = 0.61-0.79), respectively. The IRR further increased for three (0.80 [95% CI = 0.73-0.86] and 0.74 [95% CI = 0.65-0.81]) or four raters (0.84 [95% CI = 0.78-0.89] and 0.79 [95% CI = 0.71-0.85]). The IRR did not improve when QoE was assessed through a consensus rating., Conclusion: Our findings suggest that trained individuals using the GRADE approach improves reliability in comparison to intuitive judgments about the QoE and that two individual raters can reliably assess the QoE using the GRADE system., (Copyright © 2013 Elsevier Inc. All rights reserved.)

This article describes the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to classifying the direction and strength of recommendations. The strength of a recommendation, separated into strong and weak, is defined as the extent to which one can be confident that the desirable effects of an intervention outweigh its undesirable effects. Alternative terms for a weak recommendation include conditional, discretionary, or qualified. The strength of a recommendation has specific implications for patients, the public, clinicians, and policy makers. Occasionally, guideline developers may choose to make "only-in-research" recommendations. Although panels may choose not to make recommendations, this choice leaves those looking for answers from guidelines without the guidance they are seeking. GRADE therefore encourages panels to, wherever possible, offer recommendations., (Copyright © 2013. Published by Elsevier Inc.)

Summary of Findings (SoF) tables present, for each of the seven (or fewer) most important outcomes, the following: the number of studies and number of participants; the confidence in effect estimates (quality of evidence); and the best estimates of relative and absolute effects. Potentially challenging choices in preparing SoF table include using direct evidence (which may have very few events) or indirect evidence (from a surrogate) as the best evidence for a treatment effect. If a surrogate is chosen, it must be labeled as substituting for the corresponding patient-important outcome. Another such choice is presenting evidence from low-quality randomized trials or high-quality observational studies. When in doubt, a reasonable approach is to present both sets of evidence; if the two bodies of evidence have similar quality but discrepant results, one would rate down further for inconsistency. For binary outcomes, relative risks (RRs) are the preferred measure of relative effect and, in most instances, are applied to the baseline or control group risks to generate absolute risks. Ideally, the baseline risks come from observational studies including representative patients and identifying easily measured prognostic factors that define groups at differing risk. In the absence of such studies, relevant randomized trials provide estimates of baseline risk. When confidence intervals (CIs) around the relative effect include no difference, one may simply state in the absolute risk column that results fail to show a difference, omit the point estimate and report only the CIs, or add a comment emphasizing the uncertainty associated with the point estimate., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Presenting continuous outcomes in Summary of Findings tables presents particular challenges to interpretation. When each study uses the same outcome measure, and the units of that measure are intuitively interpretable (e.g., duration of hospitalization, duration of symptoms), presenting differences in means is usually desirable. When the natural units of the outcome measure are not easily interpretable, choosing a threshold to create a binary outcome and presenting relative and absolute effects become a more attractive alternative. When studies use different measures of the same construct, calculating summary measures requires converting to the same units of measurement for each study. The longest standing and most widely used approach is to divide the difference in means in each study by its standard deviation and present pooled results in standard deviation units (standardized mean difference). Disadvantages of this approach include vulnerability to varying degrees of heterogeneity in the underlying populations and difficulties in interpretation. Alternatives include presenting results in the units of the most popular or interpretable measure, converting to dichotomous measures and presenting relative and absolute effects, presenting the ratio of the means of intervention and control groups, and presenting the results in minimally important difference units. We outline the merits and limitations of each alternative and provide guidance for meta-analysts and guideline developers., (Copyright © 2013 Elsevier Inc. All rights reserved.)

GRADE requires guideline developers to make an overall rating of confidence in estimates of effect (quality of evidence-high, moderate, low, or very low) for each important or critical outcome. GRADE suggests, for each outcome, the initial separate consideration of five domains of reasons for rating down the confidence in effect estimates, thereby allowing systematic review authors and guideline developers to arrive at an outcome-specific rating of confidence. Although this rating system represents discrete steps on an ordinal scale, it is helpful to view confidence in estimates as a continuum, and the final rating of confidence may differ from that suggested by separate consideration of each domain. An overall rating of confidence in estimates of effect is only relevant in settings when recommendations are being made. In general, it is based on the critical outcome that provides the lowest confidence., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Summary of Findings (SoF) tables present, for each of the seven (or fewer) most important outcomes, the following: the number of studies and number of participants; the confidence in effect estimates (quality of evidence); and the best estimates of relative and absolute effects. Potentially challenging choices in preparing SoF tables include using direct evidence (which may have very few events) or indirect evidence (from a surrogate) as the best evidence for a treatment effect. If a surrogate is chosen, it must be labeled as substituting for the corresponding patient-important outcome. Another such choice is presenting evidence from low-quality randomised trials or high-quality observational studies. When in doubt, a reasonable approach is to present both sets of evidence; if the two bodies of evidence have similar quality but discrepant results, one would rate down further for inconsistency. For binary outcomes, relative risks (RRs) are the preferred measure of relative effect and, in most instances, are applied to the baseline or control group risks to generate absolute risks. Ideally, the baseline risks come from observational studies including representative patients and identifying easily measured prognostic factors that define groups at differing risk. In the absence of such studies, relevant randomised trials provide estimates of baseline risk. When confidence intervals (CIs) around the relative effect include no difference, one may simply state in the absolute risk column that results fail to show a difference, omit the point estimate and report only the CIs, or add a comment emphasizing the uncertainty associated with the point estimate. KEY STATEMENTS: Summary of Findings (SoF) tables provide succinct; easily digestible presentations of confidence in effect estimates (quality of evidence) and magnitude of effects. SoF tables should present the seven (or fewer) most important outcomes. These outcomes must always be patient-important outcomes and never be surrogates, although surrogates can be used to estimate effects on patient-important outcomes. SoF tables should present the highest quality evidence. When the quality of two bodies of evidence (e.g., randomised trials and observational studies) is similar, SoF tables may include summaries from both. SoF tables should include both relative and absolute effect measures, and separate estimates of absolute effect for identifiable patient groups with substantially different baseline or control group risks., (Copyright © 2013. Published by Elsevier GmbH.)

In this article we describe how to include considerations about resource utilisation when making recommendations according to the GRADE approach. We focus on challenges with rating the confidence in effect estimates (quality of evidence) and incorporating resource use into evidence profiles and Summary of Findings (SoF) tables. GRADE recommends that important differences in resource use between alternative management strategies should be included along with other important outcomes in the evidence profile and SoF table. Key steps in considering resources in making recommendations with GRADE are the identification of items of resource use that may differ between alternative management strategies and that are potentially important to decision-makers, finding evidence for the differences in resource use, making judgements regarding confidence in effect estimates using the same criteria used for health outcomes, and valuing the resource use in terms of costs for the specific setting for which recommendations are being made., (Copyright © 2013. Published by Elsevier GmbH.)

The most common reason for rating up the quality of evidence is a large effect. GRADE suggests considering rating up quality of evidence one level when methodologically rigorous observational studies show at least a two-fold reduction or increase in risk, and rating up two levels for at least a five-fold reduction or increase in risk. Systematic review authors and guideline developers may also consider rating up quality of evidence when a dose-response gradient is present, and when all plausible confounders or biases would decrease an apparent treatment effect, or would create a spurious effect when results suggest no effect. Other considerations include the rapidity of the response, the underlying trajectory of the condition and indirect evidence., (Copyright © 2013. Published by Elsevier GmbH.)

GRADE requires guideline developers to make an overall rating of confidence in estimates of effect (quality of evidence-high, moderate, low, or very low) for each important or critical outcome. GRADE suggests, for each outcome, the initial separate consideration of five domains of reasons for rating down the confidence in effect estimates, thereby allowing systematic review authors and guideline developers to arrive at an outcome-specific rating of confidence. Although this rating system represents discrete steps on an ordinal scale, it is helpful to view confidence in estimates as a continuum, and the final rating of confidence may differ from that suggested by separate consideration of each domain. An overall rating of confidence in estimates of effect is only relevant in settings when recommendations are being made. In general, it is based on the critical outcome that provides the lowest confidence., (Copyright © 2013. Published by Elsevier GmbH.)

This article introduces the GRADE approach to rating the quality of evidence. GRADE specifies four categories (high, moderate, low, and very low) that are applied to a body of evidence, not to individual studies. In the context of a systematic review, quality reflects our confidence that the estimates of the effect are correct. In the context of recommendations, quality reflects our confidence that the effect estimates are adequate to support a particular recommendation. Randomised trials begin as high quality evidence, observational studies as low quality. "Quality" as used in GRADE means more than risk of bias and so may also be compromised by imprecision, inconsistency, indirectness of study results, and publication bias. In addition, several factors can increase our confidence in an estimate of effect. GRADE provides a systematic approach for considering and reporting each of these factors. GRADE separates the process of assessing quality of evidence from the process of making recommendations. Judgments about the strength of a recommendation depend on more than just the quality of evidence., (Copyright © 2012. Published by Elsevier GmbH.)

In the GRADE approach, randomized trials are classified as high quality evidence and observational studies as low quality evidence but both can be rated down if a body of evidence is associated with a high risk of publication bias. Even when individual studies included in best-evidence summaries have a low risk of bias, publication bias can result in substantial overestimates of effect. Authors should suspect publication bias when available evidence comes from a number of small studies most of which have been commercially funded. A number of approaches based on examination of the pattern of data are available to help assess publication bias. The most popular of these is the funnel plot; all, however, have substantial limitations. Publication bias is likely frequent, and caution in the face of early results, particularly with small sample size and number of events, is warranted., (Copyright © 2012. Published by Elsevier GmbH.)

GRADE requires a clear specification of the relevant setting, population, intervention, and comparator. It also requires specification of all important outcomes - whether evidence from research studies is, or is not, available. For a particular management question, the population, intervention, and outcome should be sufficiently similar across studies so that a similar magnitude of effect is plausible. Guideline developers should specify the relative importance of the outcomes before gathering the evidence and again when evidence summaries are complete. In considering the importance of a surrogate outcome, authors should rate the importance of the patient-important outcome for which the surrogate is a substitute and subsequently rate down the quality of evidence for indirectness of outcome., (Copyright © 2012. Published by Elsevier GmbH.)

Background: Synthesizing research evidence using systematic and rigorous methods has become a key feature of evidence-based medicine and knowledge translation. Systematic reviews (SRs) may or may not include a meta-analysis depending on the suitability of available data. They are often being criticised as 'secondary research' and denied the status of original research. Scientific journals play an important role in the publication process. How they appraise a given type of research influences the status of that research in the scientific community. We investigated the attitudes of editors of core clinical journals towards SRs and their value for publication., Methods: We identified the 118 journals labelled as "core clinical journals" by the National Library of Medicine, USA in April 2009. The journals' editors were surveyed by email in 2009 and asked whether they considered SRs as original research projects; whether they published SRs; and for which section of the journal they would consider a SR manuscript., Results: The editors of 65 journals (55%) responded. Most respondents considered SRs to be original research (71%) and almost all journals (93%) published SRs. Several editors regarded the use of Cochrane methodology or a meta-analysis as quality criteria; for some respondents these criteria were premises for the consideration of SRs as original research. Journals placed SRs in various sections such as "Review" or "Feature article". Characterization of non-responding journals showed that about two thirds do publish systematic reviews., Discussion: Currently, the editors of most core clinical journals consider SRs original research. Our findings are limited by a non-responder rate of 45%. Individual comments suggest that this is a grey area and attitudes differ widely. A debate about the definition of 'original research' in the context of SRs is warranted.

This article is the first of a series providing guidance for the use of the GRADE system of rating quality of evidence and grading strength of recommendations in systematic reviews, health technology assessments, and clinical practice guidelines addressing alternative management options. The GRADE process begins with asking an explicit question, including specification of all important outcomes. After the evidence has been collected and summarised, GRADE provides explicit criteria for rating the quality of evidence that include study design, risk of bias, imprecision, inconsistency, indirectness, and magnitude of effect. Recommendations are characterised as strong or weak (alternative terms: conditional or discretionary) according to the quality of the supporting evidence and the balance between desirable and undesirable consequences of the alternative management options. GRADE suggests summarising evidence in succinct, transparent, and informative Summary of Findings tables that show the quality of evidence and the magnitude of relative and absolute effects for each important outcome and/or as evidence profiles that provide, in addition, detailed information about the reason for the quality of evidence rating. Subsequent articles in this series will address GRADE's approach to formulating questions, assessing quality of evidence, and developing recommendations., (Copyright © 2012. Published by Elsevier GmbH.)

GRADE suggests that examination of 95% confidence intervals (CIs) provides the optimal primary approach to decisions regarding imprecision. For practice guidelines, rating down the quality of evidence (i.e., confidence in estimates of effect) is required when clinical action would differ if the upper versus the lower boundary of the CI represented the truth. An exception to this rule occurs when an effect is large, and consideration of CIs alone suggests a robust effect, but the total sample size is not large and the number of events is small. Under these circumstances, one should consider rating down for imprecision. To inform this decision, one can calculate the number of patients required for an adequately powered individual trial (termed the "optimal information size" or OIS). For continuous variables, we suggest a similar process, initially considering the upper and lower limits of the CI, and subsequently calculating an OIS. Systematic reviews require a somewhat different approach. If the 95% CI excludes a relative risk (RR) of 1.0 and the total number of events or patients exceeds the OIS criterion, precision is adequate. If the 95% CI includes appreciable benefit or harm (we suggest a RR of under 0.75 or over 1.25 as a rough guide) rating down for imprecision may be appropriate even if OIS criteria are met., (Copyright © 2012. Published by Elsevier GmbH.)

In the GRADE approach, randomised trials start as high-quality evidence and observational studies as low-quality evidence, but both can be rated down if most of the relevant evidence comes from studies that suffer from a high risk of bias. Well-established limitations of randomised trials include failure to conceal allocation, failure to blind, loss to follow-up, and failure to appropriately consider the intention-to-treat principle. More recently, recognised limitations include stopping early for apparent benefit and selective reporting of outcomes according to the results. Key limitations of observational studies include use of inappropriate controls and failure to adequately adjust for prognostic imbalance. Risk of bias may vary across outcomes (e.g., loss to follow-up may be far less for all-cause mortality than for quality of life), a consideration that many systematic reviews ignore. In deciding whether to rate down for risk of bias - whether for randomised trials or observational studies-authors should not take an approach that averages across studies. Rather, for any individual outcome, when there are some studies with a high risk, and some with a low risk of bias, they should consider including only the studies with a lower risk of bias., (Copyright © 2012. Published by Elsevier GmbH.)

This article deals with inconsistency of relative, rather than absolute, treatment effects in binary/dichotomous outcomes. A body of evidence is not rated up in quality if studies yield consistent results, but may be rated down in quality if inconsistent. Criteria for evaluating consistency include similarity of point estimates, extent of overlap of confidence intervals, and statistical criteria including tests of heterogeneity and I(2). To explore heterogeneity, systematic review authors should generate and test a small number of a priori hypotheses related to patients, interventions, outcomes, and methodology. When inconsistency is large and unexplained, rating down quality for inconsistency is appropriate, particularly if some studies suggest substantial benefit, and others no effect or harm (rather than only large versus small effects). Apparent subgroup effects may be spurious. Credibility is increased if subgroup effects are based on a small number of a priori hypotheses with a specified direction; subgroup comparisons come from within rather than between studies; tests of interaction generate low p-values; and have a biological rationale., (Copyright © 2012. Published by Elsevier GmbH.)

Direct evidence comes from research that directly compares the interventions in which we are interested when applied to the populations in which we are interested and measures outcomes important to patients. Evidence can be indirect in one of four ways. First, patients may differ from those of interest (the term applicability is often used for this form of indirectness). Second, the intervention tested may differ from the intervention of interest. Decisions regarding indirectness of patients and interventions depend on an understanding of whether biological or social factors are sufficiently different that one might expect substantial differences in the magnitude of effect. Third, outcomes may differ from those of primary interest - for instance, surrogate outcomes that are not themselves important, but measured in the presumption that changes in the surrogate reflect changes in an outcome important to patients. A fourth type of indirectness, which is conceptually different from the first three, occurs when clinicians must choose between interventions that have not been tested in head to head comparisons. Making comparisons between treatments under these circumstances requires specific statistical methods and will be rated down in quality by one or two levels depending on the extent of differences between the patient populations, co-interventions, measurements of the outcome, and the methods of the trials of the candidate interventions against some other comparator., (Copyright © 2012. Published by Elsevier GmbH.)

Direct evidence comes from research that directly compares the interventions in which we are interested when applied to the populations in which we are interested and measures outcomes important to patients. Evidence can be indirect in one of four ways. First, patients may differ from those of interest (the term applicability is often used for this form of indirectness). Secondly, the intervention tested may differ from the intervention of interest. Decisions regarding indirectness of patients and interventions depend on an understanding of whether biological or social factors are sufficiently different that one might expect substantial differences in the magnitude of effect. Thirdly, outcomes may differ from those of primary interest-for instance, surrogate outcomes that are not themselves important, but measured in the presumption that changes in the surrogate reflect changes in an outcome important to patients. A fourth type of indirectness, conceptually different from the first three, occurs when clinicians must choose between interventions that have not been tested in head-to-head comparisons. Making comparisons between treatments under these circumstances requires specific statistical methods and will be rated down in quality one or two levels depending on the extent of differences between the patient populations, co-interventions, measurements of the outcome, and the methods of the trials of the candidate interventions., (Copyright © 2011 Elsevier Inc. All rights reserved.)

In the GRADE approach, randomized trials start as high-quality evidence and observational studies as low-quality evidence, but both can be rated down if a body of evidence is associated with a high risk of publication bias. Even when individual studies included in best-evidence summaries have a low risk of bias, publication bias can result in substantial overestimates of effect. Authors should suspect publication bias when available evidence comes from a number of small studies, most of which have been commercially funded. A number of approaches based on examination of the pattern of data are available to help assess publication bias. The most popular of these is the funnel plot; all, however, have substantial limitations. Publication bias is likely frequent, and caution in the face of early results, particularly with small sample size and number of events, is warranted., (Copyright © 2011 Elsevier Inc. All rights reserved.)

The most common reason for rating up the quality of evidence is a large effect. GRADE suggests considering rating up quality of evidence one level when methodologically rigorous observational studies show at least a two-fold reduction or increase in risk, and rating up two levels for at least a five-fold reduction or increase in risk. Systematic review authors and guideline developers may also consider rating up quality of evidence when a dose-response gradient is present, and when all plausible confounders or biases would decrease an apparent treatment effect, or would create a spurious effect when results suggest no effect. Other considerations include the rapidity of the response, the underlying trajectory of the condition, and indirect evidence., (Copyright © 2011 Elsevier Inc. All rights reserved.)

GRADE suggests that examination of 95% confidence intervals (CIs) provides the optimal primary approach to decisions regarding imprecision. For practice guidelines, rating down the quality of evidence (i.e., confidence in estimates of effect) is required if clinical action would differ if the upper versus the lower boundary of the CI represented the truth. An exception to this rule occurs when an effect is large, and consideration of CIs alone suggests a robust effect, but the total sample size is not large and the number of events is small. Under these circumstances, one should consider rating down for imprecision. To inform this decision, one can calculate the number of patients required for an adequately powered individual trial (termed the "optimal information size" [OIS]). For continuous variables, we suggest a similar process, initially considering the upper and lower limits of the CI, and subsequently calculating an OIS. Systematic reviews require a somewhat different approach. If the 95% CI excludes a relative risk (RR) of 1.0, and the total number of events or patients exceeds the OIS criterion, precision is adequate. If the 95% CI includes appreciable benefit or harm (we suggest an RR of under 0.75 or over 1.25 as a rough guide) rating down for imprecision may be appropriate even if OIS criteria are met., (Copyright © 2011 Elsevier Inc. All rights reserved.)

This article introduces the approach of GRADE to rating quality of evidence. GRADE specifies four categories-high, moderate, low, and very low-that are applied to a body of evidence, not to individual studies. In the context of a systematic review, quality reflects our confidence that the estimates of the effect are correct. In the context of recommendations, quality reflects our confidence that the effect estimates are adequate to support a particular recommendation. Randomized trials begin as high-quality evidence, observational studies as low quality. "Quality" as used in GRADE means more than risk of bias and so may also be compromised by imprecision, inconsistency, indirectness of study results, and publication bias. In addition, several factors can increase our confidence in an estimate of effect. GRADE provides a systematic approach for considering and reporting each of these factors. GRADE separates the process of assessing quality of evidence from the process of making recommendations. Judgments about the strength of a recommendation depend on more than just the quality of evidence., (Copyright © 2011 Elsevier Inc. All rights reserved.)

This article is the first of a series providing guidance for use of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system of rating quality of evidence and grading strength of recommendations in systematic reviews, health technology assessments (HTAs), and clinical practice guidelines addressing alternative management options. The GRADE process begins with asking an explicit question, including specification of all important outcomes. After the evidence is collected and summarized, GRADE provides explicit criteria for rating the quality of evidence that include study design, risk of bias, imprecision, inconsistency, indirectness, and magnitude of effect. Recommendations are characterized as strong or weak (alternative terms conditional or discretionary) according to the quality of the supporting evidence and the balance between desirable and undesirable consequences of the alternative management options. GRADE suggests summarizing evidence in succinct, transparent, and informative summary of findings tables that show the quality of evidence and the magnitude of relative and absolute effects for each important outcome and/or as evidence profiles that provide, in addition, detailed information about the reason for the quality of evidence rating. Subsequent articles in this series will address GRADE's approach to formulating questions, assessing quality of evidence, and developing recommendations., (Copyright © 2011 Elsevier Inc. All rights reserved.)