Clinical Queries

Nursing ◽  
2022 ◽  
Vol 52 (1) ◽  
pp. 10-11
Author(s):  
Dhivya Valluvan ◽  
Bridget Parsh
Keyword(s):  
Clinical Queries

scholarly journals Examination of the Clinical Queries and Systematic Review “hedges” in EMBASE and MEDLINE

2014 ◽  
Vol 31 (2) ◽  
pp. 27 ◽  
Author(s):  
Susan M. Bradley
Keyword(s):  
Systematic Review ◽  
Systematic Reviews ◽  
Major Error ◽  
National Library ◽  
Search Queries ◽  
Search Results ◽  
Quality Control Testing ◽  
Search Filters ◽  
The One ◽  
Clinical Queries

Introduction – This investigation sought to determine whether the methodological search filters in place as Clinical Queries limits in OvidSP EMBASE and OvidSP MEDLINE had been modified from those written by Haynes et al. and whether the translations of these in PubMed and EBSCO MEDLINE were reliable. The translated National Library of Medicine (NLM) Systematic Reviews hedges in place in OvidSP MEDLINE and EBSCO MEDLINE were also examined. Methods – Search queries were run using the Clinical Queries and Systematic Reviews hedges incorporated into OvidSP EMBASE, OvidSP MEDLINE, PubMed, and EBSCO MEDLINE to determine the reliability of these limits in comparison with the published hedge search strings. Results – Five of the OvidSP EMBASE Clinical Queries hedges produced results that were different from the published search strings. Three of the EBSCO MEDLINE and five of the PubMed translated Clinical Queries hedges yielded markedly different results (>10% difference) than those obtained using the OvidSP MEDLINE hedge counterparts. The OvidSP MEDLINE Systematic Reviews subject subset hedge was found to have a major error, which has been corrected. Discussion – Translations of hedges to appropriate syntax for other database platforms may result in significantly different search results. The platform searched should ideally be the one for which the hedges were written and tested. Regardless, the hedges in place may not be the same as the published hedge search strings. Quality control testing is needed to ensure that the hedges in place as limits are the same as those that have been published.

scholarly journals Trialstreamer: a living, automatically updated database of clinical trial reports

2020 ◽  
Author(s):  
Iain J Marshall ◽  
Benjamin Nye ◽  
Joël Kuiper ◽  
Anna Noel-Storr ◽  
Rachel Marshall ◽  
...  
Keyword(s):  
Automated System ◽  
Free Text ◽  
Gold Standard Method ◽  
Rule Based ◽  
Randomized Controlled ◽  
Structured Information ◽  
Combine Machine ◽  
Clinical Queries ◽  
Clinical Trials Registry ◽  
Extract Information

Objective Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in healthcare, but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports. Materials and Methods Trialstreamer, continuously monitors PubMed and the WHO International Clinical Trials Registry Platform (ICTRP), looking for new RCTs in humans using a validated classifier. We combine machine learning and rule-based methods to extract information from the RCT abstracts, including free-text descriptions of trial populations, interventions and outcomes (the 'PICO') and map these snippets to normalised MeSH vocabulary terms. We additionally identify sample sizes, predict the risk of bias, and extract text conveying key findings. We store all extracted data in a database which we make freely available for download, and via a search portal, which allows users to enter structured clinical queries. Results are ranked automatically to prioritize larger and higher-quality studies. Results As of May 2020, we have indexed 669,895 publications of RCTs, of which 18,485 were published in the first four months of 2020 (144/day). We additionally include 303,319 trial registrations from ICTRP. The median trial sample size in the RCTs was 66. Conclusions We present an automated system for finding and categorising RCTs. This yields a novel resource: A database of structured information automatically extracted for all published RCTs in humans. We make daily updates of this database available on our website (trialstreamer.robotreviewer.net).

PP158 The Art Of Collaboration In Guideline Development

2018 ◽  
Vol 34 (S1) ◽  
pp. 128-128
Author(s):  
Carmen Moga ◽  
Ann Scott
Keyword(s):  
Guideline Development ◽  
Multidisciplinary Care ◽  
Governance Structure ◽  
Steering Committee ◽  
Care Practice ◽  
Stakeholder Collaboration ◽  
Effective Decision Making ◽  
Practice Knowledge ◽  
Multi Stakeholder ◽  
Clinical Queries

Introduction:Developing clinical practice guidelines (CPGs) is a collaborative, multi-stakeholder enterprise. Over the last 13 years, health technology assessment (HTA) researchers from the Institute of Health Economics (IHE) partnered in a unique manner with provincial clinicians and stakeholders to develop and update CPGs using an innovative adaptation method. The complexities, intricacies, and attributes for success are presented, with emphasis on the role played by HTA resources.Methods:A governance structure (Advisory Committee, Steering Committee, Guideline Development Group) was designed to provide adequate oversight and quick, effective decision making, facilitate progress of the activities, and provide a mechanism for involving a wide variety of participants in the guideline development processes—stakeholders who represent policy, multidisciplinary care practice, knowledge translation, and research.Results:The HTA researchers served various functions and played multiple translation roles in the guideline development process: acting as a hub for connecting researchers with government to address relevant policy questions; liaising with committees to translate clinical queries into searchable questions for information specialists; preparing background documents and compiling discussion materials to expedite review by committees; connecting committees with external stakeholders such as the provincial CPG program; and bringing lay advisors into the final review process. Elements for success included effective communication, development and use of consistent methods, reliance on the highest quality of research evidence, willingness to contribute and share expertise, awareness of other initiatives and projects, transparency and openness, efficiency, flexibility, respect, enthusiasm, commitment, and patience.Conclusions:The development of CPGs requires the establishment of sophisticated multi-stakeholder collaboration and time. HTA agencies are well positioned to be an effective translation hub connecting the various stakeholders by virtue of their inherent ability to communicate in the language of policy makers, clinicians, and patients, so that all participants understand enough to add their voice to the process.

scholarly journals An analysis of clinical queries in an electronic health record search utility

2010 ◽  
Vol 79 (7) ◽  
pp. 515-522 ◽  
Author(s):  
Karthik Natarajan ◽  
Daniel Stein ◽  
Samat Jain ◽  
Noémie Elhadad
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health ◽  
Clinical Queries

The progesterone-only pill

2019 ◽  
Vol 12 (8) ◽  
pp. 425-433
Author(s):  
Zarah Yusuf ◽  
Chloe Evans ◽  
Annabel Forsythe
Keyword(s):  
Contraceptive Method ◽  
Cervical Mucus ◽  
Free Interval ◽  
Short Period ◽  
Oral Method ◽  
Clinical Queries ◽  
Current Guidelines ◽  
Effective Contraceptive Method ◽  
Effective Contraceptive

The progesterone-only pill is a popular and effective contraceptive method. It is particularly useful when either an oral method of contraception is preferred or there are contraindications to the combined pill. It is taken daily without a pill-free interval, and works mainly by increasing the cervical mucus. Desogestrel also inhibits ovulation. The progesterone-only pill is useful for those needing a reliable form of contraception within a short period, as it is effective after 48 hours when ‘quick started’. This article reviews current guidelines and answers some common clinical queries.

scholarly journals Applying the Narrow Forms of PubMed Methods-based and Topic-based Filters Increases Nephrologists’ Search Efficiency

2012 ◽  
Vol 7 (3) ◽  
pp. 95
Author(s):  
Kate Kelly
Keyword(s):  
Systematic Reviews ◽  
Citation Count ◽  
Type I ◽  
Search Performance ◽  
Clinical Question ◽  
Search Query ◽  
Search Filters ◽  
Clinical Queries ◽  
Highly Cited Articles ◽  
Highly Cited

Objective – To determine whether the use of PubMed methods-based filters and topic-based filters, alone or in combination, improves physician searching. Design – Mixed methods, survey questionnaire, comparative. Setting – Canada. Subjects – Random sample of Canadian nephrologists (n=153), responses (n=115), excluded (n=15), total (n=100). Methods – The methods are described in detail in a previously published study protocol by a subset of the authors (Shariff et al., 2010). One hundred systematic reviews on renal therapy were identified using the EvidenceUpdates service (http://plus.mcmaster.ca/EvidenceUpdates) and a clinical question was derived from each review. Randomly-selected Canadian nephrologists were randomly assigned a unique clinical question derived from the reviews and asked, by survey, to provide the search query they would use to search PubMed. The survey was administered until one valid search query for each of the one hundred questions was received. The physician search was re-executed and compared to searches where either or both methods-based and topic-based filters were applied. Nine searches for each question were conducted: the original physician search, a broad and narrow form of the clinical queries therapy filter, a broad and narrow form of the nephrology topic filter and combinations of broad and narrow forms of both filters. Significance tests of comprehensiveness (proportion of relevant articles found) and efficiency (ratio of relevant to non-relevant articles) of the filtered and unfiltered searches were conducted. The primary studies included in the systematic reviews were set as the reference standard for relevant articles. As physicians indicated they did not scan beyond two pages of default PubMed results, primary analysis was also repeated on search results restricted to the first 40 records. The ability of the filters to retrieve highly-relevant or highly-cited articles was also tested, with an article being considered highly-relevant if referenced by UpToDate and highly-cited if its citation count was greater than the median citation count of all relevant articles for that question – there was an average of eight highly-cited articles per question. To reduce the risk of type I error, the conservative method of Bonferroni was applied so that tests with a p less than 0.003 were interpreted as statistically significant. Main Results – Response rate 75%. Physician-provided search terms retrieved 46% of relevant articles and a ratio of relevant to non-relevant articles of 1:16 (p less than 0.003). Applying the narrow forms of both the nephrology and clinical queries filters together produced the greatest overall improvement, with efficiency improving by 16% and comprehensiveness remaining unchanged. Applying a narrow form of the clinical queries filter increased efficiency by 17% (p less than 0.003) but decreased comprehensiveness by 8% (p less than 0.003). No combination of search filters produced improvements in both comprehensiveness and efficiency. When results were restricted to the first 40 citations, the use of the narrow form of the clinical queries filter alone improved overall search performance – comprehensiveness improved from 13% to 26 % and efficiency from 5.5% to 23%. For highly-cited or highly-relevant articles the combined use of the narrow forms of both filters produced the greatest overall improvement in efficiency but no significant change in comprehensiveness. Conclusion – The use of PubMed search filters improves the efficiency of physician searches and saves time and frustration. Applying clinical filters for quick clinical searches can significantly improve the efficiency of physician searching. Improved search performance has the potential to enhance the transfer of research into practice and improve patient care.

scholarly journals Trialstreamer: A living, automatically updated database of clinical trial reports

2020 ◽  
Vol 27 (12) ◽  
pp. 1903-1912 ◽  
Author(s):  
Iain J Marshall ◽  
Benjamin Nye ◽  
Joël Kuiper ◽  
Anna Noel-Storr ◽  
Rachel Marshall ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Automated System ◽  
World Health ◽  
Free Text ◽  
Medical Subject Headings ◽  
Gold Standard Method ◽  
Structured Information ◽  
Health Organization ◽  
Clinical Queries ◽  
Clinical Trials Registry

Abstract Objective Randomized controlled trials (RCTs) are the gold standard method for evaluating whether a treatment works in health care but can be difficult to find and make use of. We describe the development and evaluation of a system to automatically find and categorize all new RCT reports. Materials and Methods Trialstreamer continuously monitors PubMed and the World Health Organization International Clinical Trials Registry Platform, looking for new RCTs in humans using a validated classifier. We combine machine learning and rule-based methods to extract information from the RCT abstracts, including free-text descriptions of trial PICO (populations, interventions/comparators, and outcomes) elements and map these snippets to normalized MeSH (Medical Subject Headings) vocabulary terms. We additionally identify sample sizes, predict the risk of bias, and extract text conveying key findings. We store all extracted data in a database, which we make freely available for download, and via a search portal, which allows users to enter structured clinical queries. Results are ranked automatically to prioritize larger and higher-quality studies. Results As of early June 2020, we have indexed 673 191 publications of RCTs, of which 22 363 were published in the first 5 months of 2020 (142 per day). We additionally include 304 111 trial registrations from the International Clinical Trials Registry Platform. The median trial sample size was 66. Conclusions We present an automated system for finding and categorizing RCTs. This yields a novel resource: a database of structured information automatically extracted for all published RCTs in humans. We make daily updates of this database available on our website (https://trialstreamer.robotreviewer.net).

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