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Toward a comprehensive evidence map of overview of systematic review methods: paper 1-purpose, eligibility, search and data extraction.

ABSTRACT: BACKGROUND:Overviews of systematic reviews attempt to systematically retrieve and summarise the results of multiple systematic reviews. Methods for conducting, interpreting and reporting overviews are in their infancy. To date, there has been no evidence map of the methods used in overviews, thus making it difficult to determine the gaps and priorities for methods research. Our objectives were to develop and populate a comprehensive framework of methods for conducting, interpreting and reporting overviews (stage I) and to create an evidence map by mapping studies that have evaluated overview methods to the framework (stage II). METHODS:We searched methods collections (e.g. Cochrane Methodology Register, Meth4ReSyn library, AHRQ Effective Health Care Program) to identify eligible studies for both stages of this research. In stage I, cross-sectional studies, guidance documents and commentaries that described methods proposed for, or used in, overviews were used to develop and populate the framework of methods. Drafts and multiple iterations of the framework were discussed and refined by all authors. In stage II, we identified and described studies evaluating overview methods and mapped these evaluations to the framework. RESULTS:In this paper, we present results for the four initial steps of conducting an overview: (a) specification of the purpose, objectives and scope, (b) specification of the eligibility criteria, (c) search methods and (d) data extraction. Twenty-nine studies mentioned or described methods relevant to one or more of these steps. In the developed framework, identified methods and approaches were grouped according to the steps an overview author would need to undertake. Fifteen studies evaluated identified methods, all of which mapped to the search methods step. These studies either reported the development and evaluation of a new search filter to retrieve systematic reviews or compared the performance of multiple filters. CONCLUSION:Gaps in the evaluation of methods were found for the majority of steps in the framework. More empirical studies are needed to evaluate the methods outlined and provide a comprehensive evidence map. The framework is useful for planning these evaluations and for planning methods required to deal with challenges that arise when conducting an overview.

SUBMITTER: Lunny C

PROVIDER: S-EPMC5698938 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundOverviews of systematic reviews (SRs) attempt to systematically retrieve and summarise the results of multiple systematic reviews. This is the second of two papers from a study aiming to develop a comprehensive evidence map of the methods used in overviews. Our objectives were to (a) develop a framework of methods for conducting, interpreting and reporting overviews (stage I)-the Methods for Overviews of Reviews (MOoR) framework-and (b) to create an evidence map by mapping studies that have evaluated overview methods to the framework (stage II). In the first paper, we reported findings for the four initial steps of an overview (specification of purpose, objectives and scope; eligibility criteria; search methods; data extraction). In this paper, we report the remaining steps: assessing risk of bias; synthesis, presentation and summary of the findings; and assessing certainty of the evidence arising from the overview.MethodsIn stage I, we identified cross-sectional studies, guidance documents and commentaries that described methods proposed for, or used in, overviews. Based on these studies, we developed a framework of possible methods for overviews, categorised by the steps in conducting an overview. Multiple iterations of the framework were discussed and refined by all authors. In stage II, we identified studies evaluating methods and mapped these evaluations to the framework.ResultsForty-two stage I studies described methods relevant to one or more of the latter steps of an overview. Six studies evaluating methods were included in stage II. These mapped to steps involving (i) the assessment of risk of bias (RoB) in SRs (two SRs and three primary studies, all reporting evaluation of RoB tools) and (ii) the synthesis, presentation and summary of the findings (one primary study evaluating methods for measuring overlap).ConclusionMany methods have been described for use in the latter steps in conducting an overview; however, evaluation and guidance for applying these methods is sparse. The exception is RoB assessment, for which a multitude of tools exist-several with sufficient evaluation and guidance to recommend their use. Evaluation of other methods is required to provide a comprehensive evidence map.

Project description:When studying the microbiome using next-generation sequencing, the DNA extraction method, sequencing procedures, and bioinformatic processing are crucial to obtain reliable data. Method choice has been demonstrated to strongly affect the final biological interpretation. We assessed the performance of three DNA extraction methods and two bioinformatic pipelines for bacterial microbiota profiling through 16S rRNA gene amplicon sequencing, using positive and negative controls for DNA extraction and sequencing and eight different types of high- or low-biomass samples. Performance was evaluated based on quality control passing, DNA yield, richness, diversity, and compositional profiles. All DNA extraction methods retrieved the theoretical relative bacterial abundance with a maximum 3-fold change, although differences were seen between methods, and library preparation and sequencing induced little variation. Bioinformatic pipelines showed different results for observed richness, but diversity and compositional profiles were comparable. DNA extraction methods were successful for feces and oral swabs, and variation induced by DNA extraction methods was lower than intersubject (biological) variation. For low-biomass samples, a mixture of genera present in negative controls and sample-specific genera, possibly representing biological signal, were observed. We conclude that the tested bioinformatic pipelines perform equally, with pipeline-specific advantages and disadvantages. Two out of three extraction methods performed equally well, while one method was less accurate regarding retrieval of compositional profiles. Lastly, we again demonstrate the importance of including negative controls when analyzing low-bacterial-biomass samples.IMPORTANCE Method choice throughout the workflow of a microbiome study, from sample collection to DNA extraction and sequencing procedures, can greatly affect results. This study evaluated three different DNA extraction methods and two bioinformatic pipelines by including positive and negative controls and various biological specimens. By identifying an optimal combination of DNA extraction method and bioinformatic pipeline use, we hope to contribute to increased methodological consistency in microbiota studies. Our methods were applied not only to commonly studied samples for microbiota analysis, e.g., feces, but also to more rarely studied, low-biomass samples. Microbiota composition profiles of low-biomass samples (e.g., urine and tumor biopsy specimens) were not always distinguishable from negative controls, or showed partial overlap, confirming the importance of including negative controls in microbiota studies, especially when low bacterial biomass is expected.

Project description:Conspectus There is need in the molecular simulation community to develop new quantum mechanical (QM) methods that can be routinely applied to the simulation of large molecular systems in complex, heterogeneous condensed phase environments. Although conventional methods, such as the hybrid quantum mechanical/molecular mechanical (QM/MM) method, are adequate for many problems, there remain other applications that demand a fully quantum mechanical approach. QM methods are generally required in applications that involve changes in electronic structure, such as when chemical bond formation or cleavage occurs, when molecules respond to one another through polarization or charge transfer, or when matter interacts with electromagnetic fields. A full QM treatment, rather than QM/MM, is necessary when these features present themselves over a wide spatial range that, in some cases, may span the entire system. Specific examples include the study of catalytic events that involve delocalized changes in chemical bonds, charge transfer, or extensive polarization of the macromolecular environment; drug discovery applications, where the wide range of nonstandard residues and protonation states are challenging to model with purely empirical MM force fields; and the interpretation of spectroscopic observables. Unfortunately, the enormous computational cost of conventional QM methods limit their practical application to small systems. Linear-scaling electronic structure methods (LSQMs) make possible the calculation of large systems but are still too computationally intensive to be applied with the degree of configurational sampling often required to make meaningful comparison with experiment. In this work, we present advances in the development of a quantum mechanical force field (QMFF) suitable for application to biological macromolecules and condensed phase simulations. QMFFs leverage the benefits provided by the LSQM and QM/MM approaches to produce a fully QM method that is able to simultaneously achieve very high accuracy and efficiency. The efficiency of the QMFF is made possible by partitioning the system into fragments and self-consistently solving for the fragment-localized molecular orbitals in the presence of the other fragment's electron densities. Unlike a LSQM, the QMFF introduces empirical parameters that are tuned to obtain very accurate intermolecular forces. The speed and accuracy of our QMFF is demonstrated through a series of examples ranging from small molecule clusters to condensed phase simulation, and applications to drug docking and protein-protein interactions. In these examples, comparisons are made to conventional molecular mechanical models, semiempirical methods, ab initio Hamiltonians, and a hybrid QM/MM method. The comparisons demonstrate the superior accuracy of our QMFF relative to the other models; nonetheless, we stress that the overarching role of QMFFs is not to supplant these established computational methods for problems where their use is appropriate. The role of QMFFs within the toolbox of multiscale modeling methods is to extend the range of applications to include problems that demand a fully quantum mechanical treatment of a large system with extensive configurational sampling.

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Toward a comprehensive evidence map of overview of systematic review methods: paper 1-purpose, eligibility, search and data extraction.

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