Development of a search filter to retrieve reports of interrupted time series studies from MEDLINE and PubMed

BACKGROUND

Interrupted time series (ITS) studies contribute importantly to systematic reviews of population-level interventions. We aimed to develop and validate search filters to retrieve ITS studies in MEDLINE and PubMed.

METHODS

A total of 1017 known ITS studies (published 2013-2017) were analysed using text mining to generate candidate terms. A control set of 1398 time-series studies were used to select differentiating terms. Various combinations of candidate terms were iteratively tested to generate three search filters. An independent set of 700 ITS studies was used to validate the filters' sensitivities. The filters were test-run in Ovid MEDLINE and the records randomly screened for ITS studies to determine their precision. Finally, all MEDLINE filters were translated to PubMed format and their sensitivities in PubMed were estimated.

RESULTS

Three search filters were created in MEDLINE: a precision-maximising filter with high precision (78%; 95% CI 74%-82%) but moderate sensitivity (63%; 59%-66%), most appropriate when there are limited resources to screen studies; a sensitivity-and-precision-maximising filter with higher sensitivity (81%; 77%-83%) but lower precision (32%; 28%-36%), providing a balance between expediency and comprehensiveness; and a sensitivity-maximising filter with high sensitivity (88%; 85%-90%) but likely very low precision, useful when combined with specific content terms. Similar sensitivity estimates were found for PubMed versions.

CONCLUSION

Our filters strike different balances between comprehensiveness and screening workload and suit different research needs. Retrieval of ITS studies would be improved if authors identified the ITS design in the titles.

An open competition involving thousands of competitors failed to construct useful abstract classifiers for new diagnostic test accuracy systematic reviews

There are currently no abstract classifiers, which can be used for new diagnostic test accuracy (DTA) systematic reviews to select primary DTA study abstracts from database searches. Our goal was to develop machine-learning-based abstract classifiers for new DTA systematic reviews through an open competition. We prepared a dataset of abstracts obtained through database searches from 11 reviews in different clinical areas. As the reference standard, we used the abstract lists that required manual full-text review. We randomly splitted the datasets into a train set, a public test set, and a private test set. Competition participants used the training set to develop classifiers and validated their classifiers using the public test set. The classifiers were refined based on the performance of the public test set. They could submit as many times as they wanted during the competition. Finally, we used the private test set to rank the submitted classifiers. To reduce false exclusions, we used the Fbeta measure with a beta set to seven for evaluating classifiers. After the competition, we conducted the external validation using a dataset from a cardiology DTA review. We received 13,774 submissions from 1429 teams or persons over 4 months. The top-honored classifier achieved a Fbeta score of 0.4036 and a recall of 0.2352 in the external validation. In conclusion, we were unable to develop an abstract classifier with sufficient recall for immediate application to new DTA systematic reviews. Further studies are needed to update and validate classifiers with datasets from other clinical areas.

Development of an efficient search filter to retrieve systematic reviews from PubMed

Objective:

Locating systematic reviews is essential for clinicians and researchers when creating or updating reviews and for decision-making in health care. This study aimed to develop a search filter for retrieving systematic reviews that improves upon the performance of the PubMed systematic review search filter.

Methods:

Search terms were identified from abstracts of reviews published in Cochrane Database of Systematic Reviews and the titles of articles indexed as systematic reviews in PubMed. Both the precision of the candidate terms and the number of systematic reviews retrieved from PubMed were evaluated after excluding the subset of articles retrieved by the PubMed systematic review filter. Terms that achieved a precision greater than 70% and relevant publication types indexed with MeSH terms were included in the filter search strategy.

Results:

The search strategy used in our filter added specific terms not included in PubMed's systematic review filter and achieved a 61.3% increase in the number of retrieved articles that are potential systematic reviews. Moreover, it achieved an average precision that is likely greater than 80%.

Conclusions:

The developed search filter will enable users to identify more systematic reviews from PubMed than the PubMed systematic review filter with high precision.

The NICE OECD countries' geographic search filters: Part 2-validation of the MEDLINE and Embase (Ovid) filters

Objective:

We previously developed draft MEDLINE and Embase (Ovid) geographic search filters for Organisation for Economic Co-operation and Development (OECD) countries to assess their feasibility for finding evidence about the countries. Here, we describe the validation of these search filters.

Methods:

We identified OECD country references from thirty National Institute for Health and Care Excellence (NICE) guidelines to generate gold standard sets for MEDLINE (n=2,065) and Embase (n=2,023). We validated the filters by calculating their recall against these sets. We then applied the filters to existing search strategies for three OECD-focused NICE guideline reviews (NG103 on flu vaccination, NG140 on abortion care, and NG146 on workplace health) to calculate the filters' impact on the number needed to read (NNR) of the searches.

Results:

The filters both achieved 99.95% recall against the gold standard sets. Both filters achieved 100% recall for the three NICE guideline reviews. The MEDLINE filter reduced NNR from 256 to 232 for the NG103 review, from 38 to 27 for the NG140 review, and from 631 to 591 for the NG146 review. The Embase filter reduced NNR from 373 to 341 for the NG103 review, from 101 to 76 for the NG140 review, and from 989 to 925 for the NG146 review.

Conclusion:

The NICE OECD countries' search filters are the first validated filters for the countries. They can save time for research topics about OECD countries by finding the majority of evidence about OECD countries while reducing search result volumes in comparison to no filter use.

The NICE OECD countries' geographic search filters: Part 1-methodology for developing the draft MEDLINE and Embase (Ovid) filters

Objective:

There are no existing validated search filters for the group of 37 Organisation for Economic Co-operation and Development (OECD) countries. This study describes how information specialists from the United Kingdom's National Institute for Health and Care Excellence (NICE) developed and evaluated novel OECD countries’ geographic search filters for MEDLINE and Embase (Ovid) to improve literature search effectiveness for evidence about OECD countries.

Methods:

We created the draft filters using an alternative approach to standard filter construction. They are composed entirely of geographic subject headings and are designed to retain OECD country evidence by excluding non-OECD country evidence using the NOT Boolean operator. To evaluate the draft filters’ effectiveness, we used MEDLINE and Embase literature searches for three NICE guidelines that retrieved >5,000 search results. A 10% sample of the excluded references was screened to check that OECD country evidence was not inadvertently excluded.

Results:

The draft MEDLINE filter reduced results for each NICE guideline by 9.5% to 12.9%. In Embase, search results were reduced by 10.7% to 14%. Of the sample references, 7 of 910 (0.8%) were excluded inadvertently. These references were from a guideline about looked-after minors that concerns both OECD and non-OECD countries.

Conclusion:

The draft filters look promising—they reduced search result volumes while retaining most OECD country evidence from MEDLINE and Embase. However, we advise caution when using them in topics about both non-OECD and OECD countries. We have created final versions of the search filters and will validate them in a future study.

Comprehensive search filters for retrieving publications on nonhuman primates for literature reviews (filterNHP)

Nonhuman primates (NHPs) are widely studied across many scientific disciplines using a variety of techniques in diverse environments. Due to the wide scope of NHP research, substantial overlap in research topics and questions can occur, whose resulting scientific evidence is synthesized by literature reviews. Identifying all relevant research on a particular topic involving NHPs can be difficult and time consuming. By adopting objective search development techniques from systematic reviews, we developed search filters to detect all scientific publications involving NHPs in PubMed, PsycINFO (via EBSCOhost), and Web of Science. We compared the performance of our comprehensive NHP search filters to search strings typical of a novice database user (i.e., NHP simple search strings) and validated their sensitivity by combining these searches with a topic search of cortisol related studies. For all comparisons, our comprehensive NHP search filters retrieved considerably more scientific publications than the NHP simple search strings. Importantly, our comprehensive NHP search filters are easy to use (text can be copied and pasted into the database search engine) and detect the most recent publications that have yet to be indexed by the bibliographic databases queried. Additionally, we developed filterNHP, an R package and web-based application (https://filterNHP.dpz.eu), for researchers interested in literature searches involving a taxonomic sub-group of NHPs. filterNHP alleviates time necessary for adapting our comprehensive NHP search filters for a particular NHP sub-group by automatizing the creation of these search filters. Altogether, our comprehensive NHP search filters and those for taxonomic sub-groups generated by filterNHP will enable swift and easy retrieval of the available scientific literature involving NHPs, and thereby help enhance the quality of new NHP literature reviews that guide future scientific research (new experiments) and public policy (e.g., on welfare and conservation).

[How do authors of systematic reviews restrict their literature searches when only studies from Germany should be included?]

INTRODUCTION

The use of search filters (e. g. for study types) facilitates the process of literature searching. Regional limits might be helpful depending on the research question. Regional search filters are already available for some regions, but not for Germany. Our aim is to give an overview of applied search strategies in systematic reviews (SRs) focusing on Germany.

METHODS

We searched Medline (via Pubmed) applying a focused search strategy to identify SRs focusing on Germany in January 2016. Study selection and data extraction were performed by two reviewers independently. The search strategies with a focus on Germany were analyzed in terms of reasonableness and completeness relying on the Peer Review of Electronic Search Strategies (PRESS) criteria. A narrative evidence synthesis was performed.

RESULTS

In total, 36 SRs (13 written in English) were included. 78% were published in 2012 or later. The majority (89%) of SRs utilized at least two different sources for their search with databases and checking references being the most common. 17 SRs did not use any truncations, ten SRs did not restrict their search to Germany, six SRs reported to have searched for German OR Germany. Only ten articles searched for the term Germany (occasionally jointly with the term Deutschland) without any use of an adjective such as German.

DISCUSSION

There is a high interest in regionally focused SRs. The identified search strategies revealed a need for improvement. It would be helpful to develop a regional search filter for Germany that is able to identify studies performed in Germany.

Garbage in, Garbage Out: Data Collection, Quality Assessment and Reporting Standards for Social Media Data Use in Health Research, Infodemiology and Digital Disease Detection

Background

Social media have transformed the communications landscape. People increasingly obtain news and health information online and via social media. Social media platforms also serve as novel sources of rich observational data for health research (including infodemiology, infoveillance, and digital disease detection detection). While the number of studies using social data is growing rapidly, very few of these studies transparently outline their methods for collecting, filtering, and reporting those data. Keywords and search filters applied to social data form the lens through which researchers may observe what and how people communicate about a given topic. Without a properly focused lens, research conclusions may be biased or misleading. Standards of reporting data sources and quality are needed so that data scientists and consumers of social media research can evaluate and compare methods and findings across studies.

Objective

We aimed to develop and apply a framework of social media data collection and quality assessment and to propose a reporting standard, which researchers and reviewers may use to evaluate and compare the quality of social data across studies.

Methods

We propose a conceptual framework consisting of three major steps in collecting social media data: develop, apply, and validate search filters. This framework is based on two criteria: retrieval precision (how much of retrieved data is relevant) and retrieval recall (how much of the relevant data is retrieved). We then discuss two conditions that estimation of retrieval precision and recall rely on—accurate human coding and full data collection—and how to calculate these statistics in cases that deviate from the two ideal conditions. We then apply the framework on a real-world example using approximately 4 million tobacco-related tweets collected from the Twitter firehose.

Results

We developed and applied a search filter to retrieve e-cigarette–related tweets from the archive based on three keyword categories: devices, brands, and behavior. The search filter retrieved 82,205 e-cigarette–related tweets from the archive and was validated. Retrieval precision was calculated above 95% in all cases. Retrieval recall was 86% assuming ideal conditions (no human coding errors and full data collection), 75% when unretrieved messages could not be archived, 86% assuming no false negative errors by coders, and 93% allowing both false negative and false positive errors by human coders.

Conclusions

This paper sets forth a conceptual framework for the filtering and quality evaluation of social data that addresses several common challenges and moves toward establishing a standard of reporting social data. Researchers should clearly delineate data sources, how data were accessed and collected, and the search filter building process and how retrieval precision and recall were calculated. The proposed framework can be adapted to other public social media platforms.

High-performance information search filters for CKD content in PubMed, Ovid MEDLINE, and EMBASE

BACKGROUND

Finding relevant articles in large bibliographic databases such as PubMed, Ovid MEDLINE, and EMBASE to inform care and future research is challenging. Articles relevant to chronic kidney disease (CKD) are particularly difficult to find because they are often published under different terminology and are found across a wide range of journal types.

STUDY DESIGN

We used computer automation within a diagnostic test assessment framework to develop and validate information search filters to identify CKD articles in large bibliographic databases.

SETTING & PARTICIPANTS

22,992 full-text articles in PubMed, Ovid MEDLINE, or EMBASE.

INDEX TEST

1,374,148 unique search filters.

REFERENCE TEST

We established the reference standard of article relevance to CKD by manual review of all full-text articles using prespecified criteria to determine whether each article contained CKD content or not. We then assessed filter performance by calculating sensitivity, specificity, and positive predictive value for the retrieval of CKD articles. Filters with high sensitivity and specificity for the identification of CKD articles in the development phase (two-thirds of the sample) were then retested in the validation phase (remaining one-third of the sample).

RESULTS

We developed and validated high-performance CKD search filters for each bibliographic database. Filters optimized for sensitivity reached at least 99% sensitivity, and filters optimized for specificity reached at least 97% specificity. The filters were complex; for example, one PubMed filter included more than 89 terms used in combination, including "chronic kidney disease," "renal insufficiency," and "renal fibrosis." In proof-of-concept searches, physicians found more articles relevant to the topic of CKD with the use of these filters.

LIMITATIONS

As knowledge of the pathogenesis of CKD grows and definitions change, these filters will need to be updated to incorporate new terminology used to index relevant articles.

CONCLUSIONS

PubMed, Ovid MEDLINE, and EMBASE can be filtered reliably for articles relevant to CKD. These high-performance information filters are now available online and can be used to better identify CKD content in large bibliographic databases.