Dataset Information

Methodology for constructing guidance.

ABSTRACT: Although guidance exists for the use of many laboratory tests in a wide range of clinical situations, this guidance is spread among a range of literature sources, and is often directed at laboratory specialists rather than test users. Individual general practices display large variations in standardised test requesting, yet much of their testing activity involves a relatively small range of tests. This paper describes a methodological approach to review the available evidence and guidance and to extract relevant primary research work to examine a range of testing scenarios in general practice, with the aim of formulating guidance based on the best available evidence or consensus opinions.

SUBMITTER: Smellie WS

PROVIDER: S-EPMC1770610 | biostudies-literature | 2005 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Methodology for constructing guidance.

Smellie W S A WS Finnigan D I DI Wilson D D Freedman D D McNulty C A M CA Clark G G

Journal of clinical pathology 20050301 3

Although guidance exists for the use of many laboratory tests in a wide range of clinical situations, this guidance is spread among a range of literature sources, and is often directed at laboratory specialists rather than test users. Individual general practices display large variations in standardised test requesting, yet much of their testing activity involves a relatively small range of tests. This paper describes a methodological approach to review the available evidence and guidance and to ...[more]

PMID: 15735154

Similar Datasets

Project description:ObjectiveTo identify evidence on the reporting quality of consensus methodology and to select potential checklist items for the ACcurate COnsensus Reporting Document (ACCORD) project to develop a consensus reporting guideline.DesignSystematic review.Data sourcesEmbase, MEDLINE, Web of Science, PubMed, Cochrane Library, Emcare, Academic Search Premier and PsycINFO from inception until 7 January 2022.Eligibility criteriaStudies, reviews and published guidance addressing the reporting quality of consensus methodology for improvement of health outcomes in biomedicine or clinical practice. Reports of studies using or describing consensus methods but not commenting on their reporting quality were excluded. No language restrictions were applied.Data extraction and synthesisScreening and data extraction of eligible studies were carried out independently by two authors. Reporting quality items addressed by the studies were synthesised narratively.ResultsEighteen studies were included: five systematic reviews, four narrative reviews, three research papers, three conference abstracts, two research guidance papers and one protocol. The majority of studies indicated that the quality of reporting of consensus methodology could be improved. Commonly addressed items were: consensus panel composition; definition of consensus and the threshold for achieving consensus. Items least addressed were: public patient involvement (PPI); the role of the steering committee, chair, cochair; conflict of interest of panellists and funding. Data extracted from included studies revealed additional items that were not captured in the data extraction form such as justification of deviation from the protocol or incentives to encourage panellist response.ConclusionThe results of this systematic review confirmed the need for a reporting checklist for consensus methodology and provided a range of potential checklist items to report. The next step in the ACCORD project builds on this systematic review and focuses on reaching consensus on these items to develop the reporting guideline.Protocol registrationhttps://osf.io/2rzm9.

Project description:The increasing growth of literature in biodiversity presents challenges to users who need to discover pertinent information in an efficient and timely manner. In response, text mining techniques offer solutions by facilitating the automated discovery of knowledge from large textual data. An important step in text mining is the recognition of concepts via their linguistic realisation, i.e., terms. However, a given concept may be referred to in text using various synonyms or term variants, making search systems likely to overlook documents mentioning less known variants, which are albeit relevant to a query term. Domain-specific terminological resources, which include term variants, synonyms and related terms, are thus important in supporting semantic search over large textual archives. This article describes the use of text mining methods for the automatic construction of a large-scale biodiversity term inventory. The inventory consists of names of species, amongst which naming variations are prevalent. We apply a number of distributional semantic techniques on all of the titles in the Biodiversity Heritage Library, to compute semantic similarity between species names and support the automated construction of the resource. With the construction of our biodiversity term inventory, we demonstrate that distributional semantic models are able to identify semantically similar names that are not yet recorded in existing taxonomies. Such methods can thus be used to update existing taxonomies semi-automatically by deriving semantically related taxonomic names from a text corpus and allowing expert curators to validate them. We also evaluate our inventory as a means to improve search by facilitating automatic query expansion. Specifically, we developed a visual search interface that suggests semantically related species names, which are available in our inventory but not always in other repositories, to incorporate into the search query. An assessment of the interface by domain experts reveals that our query expansion based on related names is useful for increasing the number of relevant documents retrieved. Its exploitation can benefit both users and developers of search engines and text mining applications.

Dataset Information

Methodology for constructing guidance.

Publications

Methodology for constructing guidance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets