Development and evaluation of a search filter to identify prognostic factor studies in Ovid MEDLINE.

BACKGROUND: Systematic reviews (SRs) are valuable resources as they address specific clinical questions by summarizing all existing relevant studies. However, finding all information to include in systematic reviews can be challenging. Methodological search filters have been developed to find articles related to specific clinical questions. To our knowledge, no filter exists for finding studies on the role of prognostic factor (PF). We aimed to develop and evaluate a search filter to identify PF studies in Ovid MEDLINE that has maximum sensitivity. METHODS: We followed current recommendations for the development of a search filter by first identifying a reference set of PF studies included in relevant systematic reviews on the topic, and by selecting search terms using a word frequency analysis complemented with an expert panel discussion. We evaluated filter performance using the relative recall methodology. RESULTS: We constructed a reference set of 73 studies included in six systematic reviews from a larger sample. After completing a word frequency analysis using the reference set studies, we compiled a list of 80 of the frequent methodological terms. This list of terms was evaluated by the Delphi panel for inclusion in the filter, resulting in a final set of 8 appropriate terms. The consecutive connection of these terms with the Boolean operator OR produced the filter. We then evaluated the filter using the relative recall method against the reference set, comparing the references included in the SRs with our new search using the filter. The overall sensitivity of the filter was calculated to be 95%, while the overall specificity was 41%. The precision of the filter varied considerably, ranging from 0.36 to 17%. The NNR (number needed to read) value varied largely from 6 to 278. The time saved by using the filter ranged from 13-70%. CONCLUSIONS: We developed a search filter for OVID-Medline with acceptable performance that could be used in systematic reviews of PF studies. Using this filter could save as much as 40% of the title and abstract screening task. The specificity of the filter could be improved by defining additional terms to be included, although it is important to evaluate any modification to guarantee the filter is still highly sensitive.

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).

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.

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.

[Publications German-speaking countries in high impact journals: development and validation of a search filter]. / Publikationen aus dem deutschsprachigen Raum in Zeitschriften mit hohem Impact Factor: Entwicklung und Validierung eines Suchfilters.

Publications German-speaking countries in high impact journals: development and validation of a search filter Abstract. BACKGROUND: The number of publications in journals with a high impact factor is an indication of a discipline's participation in international discourse. A search filter allows reliable and reproducible searches for specific publications. AIM: Development and validation of a geographic search filter for publications by nursing scientists affiliated to German-speaking countries in nursing journals with a high impact factor. METHODS: The search filter was objectively developed following several steps: (i) creation of a development and a validation set, each consisting of relevant and non-relevant publications, (ii) generation of the search filter by means of text analysis of the development set, (iii) internal validation based on the development set and (iv) external validation using the validation set. The validity was examined regarding several accuracy parameters, e. g. sensitivity, specificity, positive predictive value (PPV) and number needed to read (NNR). RESULTS: The search filter correctly identified 22 of 30 relevant and 16 of 21 non-relevant publications in the development set: sensitivity 80 % (95 % CI 66 - 94), specificity 76 % (95 % CI 58 - 94), PPV 83 % (95 % CI 69 - 97). External validation yielded similar or better results: sensitivity 81 % (95 % CI 67 - 96), specificity 88 % (95 % CI 71 - 100), PPV 88 % (95 % CI 75 - 100). The NNR was 1.2 and 1.1, respectively. CONCLUSIONS: The search filter has the potential to identify the intended publications.

Development and validation of a MEDLINE search filter/hedge for degenerative cervical myelopathy.

BACKGROUND: Degenerative cervical myelopathy (DCM) is a common condition with many unmet clinical needs. Pooled analysis of studies is an important tool for advancing medical understanding. This process starts with a systematic search of the literature. Identification of studies in DCM is challenged by a number of factors, including non-specific terminology and index terms. Search filters or HEDGEs, are search strings developed and validated to optimise medical literature searches. We aimed to develop a search filter for DCM for the MEDLINE database. METHODS: The diagnostic test assessment framework of a "development dataset" and seperate "validation dataset" was used. The development dataset was formed by hand searching four leading spinal journals (Spine, Journal of Neurosurgery Spine, Spinal Cord and Journal of Spinal Disorders and Techniques) in 2005 and 2010. The search filter was initially developed focusing on sensitivity and subsequently refined using NOT functions to improve specificity. One validation dataset was formed from DCM narrative and systematic review articles and the second, articles published in April of 1989, 1993, 1997, 2001, 2005, 2009, 2013 and 2017 retrieved via the search MeSH term 'Spine'. Metrics of sensitivity, specificity, precision and accuracy were used to test performance. RESULTS: Hand searching identified 77/1094 relevant articles for 2005 and 55/1199 for 2010. We developed a search hedge with 100% sensitivity and a precision of 30 and 29% for the 2005 and 2010 development datasets respectively. For the selected time periods, EXP Spine returned 2113 publications and 30 were considered relevant. The search filter identified all 30 relevant articles, with a specificity of 94% and precision of 20%. Of the 255 references listed in the narrative index reviews, 225 were indexed in MEDLINE and 165 (73%) were relevant articles. All relevant articles were identified and accuracy ranged from 67 to 97% over the three reviews. Of the 42 articles returned from 3 recent systematic reviews, all were identified by the filter. CONCLUSIONS: We have developed a highly sensitive hedge for the research of DCM. Whilst precision is similarly low as other hedges, this search filter can be used as an adjunct for DCM search strategies.

Paramedic literature search filters: optimised for clinicians and academics.

BACKGROUND: Search filters aid clinicians and academics to accurately locate literature. Despite this, there is no search filter or Medical Subject Headings (MeSH) term pertaining to paramedics. Therefore, the aim of this study was to create two filters to meet to different needs of paramedic clinicians and academics. METHODS: We created a gold standard from a reference set, which we measured against single terms and search filters. The words and phrases used stemmed from selective exclusion of terms from the previously published Prehospital Search Filter 2.0 as well as a Delphi session with an expert panel of paramedic researchers. Independent authors deemed articles paramedic-relevant or not following an agreed definition. We measured sensitivity, specificity, accuracy and number needed to read (NNR). RESULTS: We located 2102 articles of which 431 (20.5%) related to paramedics. The performance of single terms was on average of high specificity (97.1% (Standard Deviation 7.4%), but of poor sensitivity (12.0%, SD 18.7%). The NNR ranged from 1 to 8.6. The sensitivity-maximising search filter yielded 98.4% sensitivity, with a specificity of 74.3% and a NNR of 2. The specificity-maximising filter achieved 88.3% in specificity, which only lowered the sensitivity to 94.7%, and thus a NNR of 1.48. CONCLUSIONS: We have created the first two paramedic specific search filters, one optimised for sensitivity and one optimised for specificity. The sensitivity-maximising search filter yielded 98.4% sensitivity, and a NNR of 2. The specificity-maximising filter achieved 88.3% in specificity, which only lowered the sensitivity to 94.7%, and a NNR of 1.48. A paramedic MeSH term is needed.

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.

Finding Qualitative Research Evidence for Health Technology Assessment.

Health technology assessment (HTA) agencies increasingly use reviews of qualitative research as evidence for evaluating social, experiential, and ethical aspects of health technologies. We systematically searched three bibliographic databases (MEDLINE, CINAHL, and Social Science Citation Index [SSCI]) using published search filters or "hedges" and our hybrid filter to identify qualitative research studies pertaining to chronic obstructive pulmonary disease and early breast cancer. The search filters were compared in terms of sensitivity, specificity, and precision. Our screening by title and abstract revealed that qualitative research constituted only slightly more than 1% of all published research on each health topic. The performance of the published search filters varied greatly across topics and databases. Compared with existing search filters, our hybrid filter demonstrated a consistently high sensitivity across databases and topics, and minimized the resource-intensive process of sifting through false positives. We identify opportunities for qualitative health researchers to improve the uptake of qualitative research into evidence-informed policy making.

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.

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.

Laboratory animals search filter for different literature databases: PubMed, Embase, Web of Science and PsycINFO.

Systematic reviews are important tools in animal research, but the ever-increasing number of studies makes retrieval of all relevant publications challenging. Search filters aid in retrieving as many animal studies as possible. In this paper we provide updated and expanded versions of the SYRCLE animal filters for PubMed and Embase. We provide the Embase filter for both Embase.com and via Ovid. Furthermore, we provide new animal search filters for Web of Science (WoS) and APA PsycINFO via psycnet.apa.org and via Ovid. Compared with previous versions, the new filters retrieved 0.5-47.1% (19 references for PubMed, 837 for WoS) more references in a real-life example. All filters retrieved additional references, comprising multiple relevant reviews. A random sample from WoS found at least one potentially relevant primary study. These animal search filters facilitate identifying as many animal studies as possible while minimising the number of non-animal studies.

A validation study revealed differences in design and performance of MEDLINE search filters for qualitative research.

OBJECTIVES: Several search filters exist to identify qualitative research, but so far none of them has been validated with an independent set of relevant references irrespective of a medical topic. The objective of this study was to provide a comparative overview of validation results for various MEDLINE search filters. STUDY DESIGN AND SETTING: Search filters were tested for plausibility. A relative recall approach was used to generate a gold standard based on an overview of systematic reviews of qualitative studies. For each review, the included qualitative studies were collected and checked for MEDLINE-indexing. The body of indexed articles yielded the gold standard. Validation tests were conducted to determine whether the references of the gold standard could be identified with the respective search filters. RESULTS: Thirteen search filters were validated in MEDLINE. One search filter by Wong et al. (2004) was found to be the most sensitive (93.63%). While medical subject heading "qualitative research" achieved the best precision (2.15%), sensitivity was the lowest (22.56%). University of Texas provided the best balanced search filter with a sensitivity of 81.96% and a precision of 0.80%. CONCLUSION: Search filters to identify qualitative research in MEDLINE differ greatly in design and performance. The selection of the appropriate search filter depends on project-specific demands and resources.

A validated search filter for the identification of endocrine disruptors based on the ECHA/EFSA guidance recommendations.

A guidance document for the identification of endocrine disruptors (EDs) in the regulatory assessment of plant protection products (PPP) and biocidal products (BP) has been published by the European Chemical Agency (ECHA) and the European Food Safety Authority (EFSA). The ECHA/EFSA guidance, mainly addressing EATS (estrogen, androgen, thyroid, steroidogenesis) modalities, is intended to guide applicants and assessors of the competent regulatory authorities on the implementation of the scientific criteria for the determination of ED properties pursuant to the recently implemented PPP (EU 2018/605) and BP (EU 2017/2100) EU Regulations. In this study, a search filter for targeted literature search in context of assessing if a substance can be identified as an ED relevant for human health was developed and validated. Development of the search filter was based on the search strategy presented in the ECHA/EFSA guidance and using the estrogenic chemical Bisphenol AF (BPAF) as a model substance. Information specialists from two independent institutions developed refined search filters based on the suggested original search strategy published (ECHA/EFSA guidance - Appendix F). Articles identified by a systematic literature search for BPAF were screened for relevance with inclusion and exclusion criteria by two independent reviewers obtaining positive (relevant) and negative (irrelevant) controls. The developed search filter was quantitatively evaluated in terms of sensitivity, specificity and precision based on the positive and negative controls. The developed filter was then validated for T modality by its application to the known thyroid-disruptor perchlorate. The result is a sensitive search filter with sufficient specificity, which can be applied for all chemicals where a targeted literature search is needed to assess and identify ED properties of chemicals with relevance for humans. Future application of the filter to a broader range of chemicals may identify further points of improvement.

Validation of five search filters for retrieval of clinical practice guidelines produced low precision.

OBJECTIVES: The aim of the study was to validate search filters for retrieval of clinical practice guidelines (CPGs) in MEDLINE, Embase, and PubMed. STUDY DESIGN AND SETTING: A search for filters for identifying CPGs was conducted in Google and the InterTASC Information Specialists Sub-Group Search Filter Resource. To retrieve a random sample of CPGs to test sensitivity and precision of the filters, we used the TRIP and Epistemonikos databases. The citations were screened independently by two researchers. The sensitivity and precision were calculated. RESULTS: Five search filters were retrieved: two from the Canadian Agency for Drugs and Technologies in Health (CADTH), two from the University of Texas, and one from the MD Anderson Cancer Center Library. A total of 478 records were screened to identify 109 CPGs, which comprised the sample for testing sensitivity and precision. The sensitivity ranged from 87% to 98% for the five search filters and very low precision (<1%) across all databases. CONCLUSION: Knowledge users who are interested in retrieving all relevant CPGs can use the CADTH broad filter with the highest sensitivity. However, our analysis shows that it remains difficult to efficiently identify CPGs because of low precision of five search filters. We recommend searching guideline-specific resources as a more time-efficient approach than searching bibliographic databases.

A validation study revealed differences in design and performance of search filters for qualitative research in PsycINFO and CINAHL.

OBJECTIVES: Search filters can support qualitative evidence of information retrieval. Various search filters are available for the bibliographic databases PsycINFO and CINAHL. To date, no comparative overview of validation results of search filters verified with an independent gold standard exists. STUDY DESIGN AND SETTING: Identified search filters for PsycINFO and CINAHL were tested for plausibility. Gold standards were generated according to the relative recall approach using references included in an overview of systematic reviews of qualitative studies. All included references were collected and checked for indexing in PsycINFO and CINAHL. Validation tests for each search filter were conducted in both databases to determine whether the references of the gold standards could be retrieved or not. RESULTS: Twelve search filters for PsycINFO and fifteen for CINAHL were validated. The complexity and design of these search filters vary, as well as the validation results for the databases. When locating primary studies of qualitative research, the best sensitivity and precision ratio (among filters with a sensitivity of >80%) was achieved with a filter by McKibbon et al. for PsycINFO and a filter by Wilczynski et al. for CINAHL. CONCLUSION: Project-specific requirements and resources influence the choice of a specific search filter for PsycINFO and CINAHL.

Protocol: a systematic review of studies developing and/or evaluating search strategies to identify prognosis studies.

BACKGROUND: Prognosis research is on the rise, its importance recognised because chronic health conditions and diseases are increasingly common and costly. Prognosis systematic reviews are needed to collate and synthesise these research findings, especially to help inform effective clinical decision-making and healthcare policy. A detailed, comprehensive search strategy is central to any systematic review. However, within prognosis research, this is challenging due to poor reporting and inconsistent use of available indexing terms in electronic databases. Whilst many published search filters exist for finding clinical trials, this is not the case for prognosis studies. This systematic review aims to identify and compare existing methodological filters developed and evaluated to identify prognosis studies of any of the three main types: overall prognosis, prognostic factors, and prognostic [risk prediction] models. METHODS: Primary studies reporting the development and/or evaluation of methodological search filters to retrieve any type of prognosis study will be included in this systematic review. Multiple electronic bibliographic databases will be searched, grey literature will be sought from relevant organisations and websites, experts will be contacted, and citation tracking of key papers and reference list checking of all included papers will be undertaken. Titles will be screened by one person, and abstracts and full articles will be reviewed for inclusion independently by two reviewers. Data extraction and quality assessment will also be undertaken independently by two reviewers with disagreements resolved by discussion or by a third reviewer if necessary. Filters' characteristics and performance metrics reported in the included studies will be extracted and tabulated. To enable comparisons, filters will be grouped according to database, platform, type of prognosis study, and type of filter for which it was intended. DISCUSSION: This systematic review will identify all existing validated prognosis search filters and synthesise evidence about their applicability and performance. These findings will identify if current filters provide a proficient means of searching electronic bibliographic databases or if further prognosis filters are needed and can feasibly be developed for systematic searches of prognosis studies.

[How do authors of systematic reviews restrict their literature searches when only studies from Germany should be included?] / Wie beschränken Autoren systematischer Reviews ihre Literatursuchen, wenn nur Studien aus Deutschland eingeschlossen werden sollen?

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.