MEDLINE search retrieval issues: A longitudinal query analysis of five vendor platforms.
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ABSTRACT:
SUBMITTER: Burns CS
PROVIDER: S-EPMC8101950 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Objective:Hypothetically, content in MEDLINE records is consistent across multiple platforms. Though platforms have different interfaces and requirements for query syntax, results should be similar when the syntax is controlled for across the platforms. The authors investigated how search result counts varied when searching records among five MEDLINE platforms. Methods:We created 29 sets of search queries targeting various metadata fields and operators. Within search sets, we adapted 5 distinct, compatible queries to search 5 MEDLINE platforms (PubMed, ProQuest, EBSCOhost, Web of Science, and Ovid), totaling 145 final queries. The 5 queries were designed to be logically and semantically equivalent and were modified only to match platform syntax requirements. We analyzed the result counts and compared PubMed's MEDLINE result counts to result counts from the other platforms. We identified outliers by measuring the result count deviations using modified z-scores centered around PubMed's MEDLINE results. Results:Web of Science and ProQuest searches were the most likely to deviate from the equivalent PubMed searches. EBSCOhost and Ovid were less likely to deviate from PubMed searches. Ovid's results were the most consistent with PubMed's but appeared to apply an indexing algorithm that resulted in lower retrieval sets among equivalent searches in PubMed. Web of Science exhibited problems with exploding or not exploding Medical Subject Headings (MeSH) terms. Conclusion:Platform enhancements among interfaces affect record retrieval and challenge the expectation that MEDLINE platforms should, by default, be treated as MEDLINE. Substantial inconsistencies in search result counts, as demonstrated here, should raise concerns about the impact of platform-specific influences on search results.
| S-EPMC6579582 | biostudies-literature
Project description:ObjectiveTo determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant. DESIGN AND MEASUREMENTS A direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology. RESULTS Citation-based algorithms were more effective than noncitation-based algorithms at identifying important articles. The most effective strategies were simple citation count and PageRank, which on average identified over six important articles in the first 100 results compared to 0.85 for the best noncitation-based algorithm (p < 0.001). The authors saw similar differences between citation-based and noncitation-based algorithms at 10, 20, 50, 200, 500, and 1,000 results (p < 0.001). Citation lag affects performance of PageRank more than simple citation count. However, in spite of citation lag, citation-based algorithms remain more effective than noncitation-based algorithms. CONCLUSION Algorithms that have proved successful on the World Wide Web can be applied to biomedical information retrieval. Citation-based algorithms can help identify important articles within large sets of relevant results. Further studies are needed to determine whether citation-based algorithms can effectively meet actual user information needs.
| S-EPMC1380202 | biostudies-literature
Project description:ObjectiveHealth information retrieval (HIR) on the Internet has become an important practice for millions of people, many of whom have problems forming effective queries. We have developed and evaluated a tool to assist people in health-related query formation.DesignWe developed the Health Information Query Assistant (HIQuA) system. The system suggests alternative/additional query terms related to the user's initial query that can be used as building blocks to construct a better, more specific query. The recommended terms are selected according to their semantic distance from the original query, which is calculated on the basis of concept co-occurrences in medical literature and log data as well as semantic relations in medical vocabularies.MeasurementsAn evaluation of the HIQuA system was conducted and a total of 213 subjects participated in the study. The subjects were randomized into 2 groups. One group was given query recommendations and the other was not. Each subject performed HIR for both a predefined and a self-defined task.ResultsThe study showed that providing HIQuA recommendations resulted in statistically significantly higher rates of successful queries (odds ratio = 1.66, 95% confidence interval = 1.16-2.38), although no statistically significant impact on user satisfaction or the users' ability to accomplish the predefined retrieval task was found.ConclusionProviding semantic-distance-based query recommendations can help consumers with query formation during HIR.
| S-EPMC1380203 | biostudies-literature
Project description:ObjectivesTo create user-friendly search filters with high sensitivity, specificity, and precision to identify articles on geriatric medicine in Medline.DesignA diagnostic test assessment framework was used. A reference set of 2255 articles was created by hand-searching 22 biomedical journals in Medline, and each article was labeled as 'relevant', 'not relevant', or 'possibly relevant' for geriatric medicine. From the relevant articles, search terms were identified to compile different search strategies. The articles retrieved by the various search strategies were compared with articles from the reference set as the index test to create the search filters.MeasuresSensitivity, specificity, precision, accuracy, and number-needed-to-read (NNR) were calculated by comparing the results retrieved by the different search strategies with the reference set.ResultsThe most sensitive search filter had a sensitivity of 94.8%, a specificity of 88.7%, a precision of 73.0%, and an accuracy of 90.2%. It had an NNR of 1.37. The most specific search filter had a specificity of 96.6%, a sensitivity of 69.1%, a precision of 86.6%, and an accuracy of 89.9%. It had an NNR of 1.15.ConclusionThese geriatric search filters simplify searching for relevant literature and therefore contribute to better evidence-based practice. The filters are useful to both the clinician who wants to find a quick answer to a clinical question and the researcher who wants to find as many relevant articles as possible without retrieving too many irrelevant articles.
| S-EPMC3341784 | biostudies-literature
Project description:MotivationDue to the rapid growth of the genetic database size, genealogical search, a process of inferring familial relatedness by identifying DNA matches, has become a viable approach to help individuals finding missing family members or law enforcement agencies locating suspects. A fast and accurate method is needed to search an out-of-database individual against millions of individuals. Most existing approaches only offer all-vs-all within panel match. Some prototype algorithms offer one-vs-all query from out-of-panel individual, but they do not tolerate errors.ResultsA new method, random projection-based identical-by-descent (IBD) detection (RaPID) query, is introduced to make fast genealogical search possible. RaPID-Query identifies IBD segments between a query haplotype and a panel of haplotypes. By integrating matches over multiple PBWT indexes, RaPID-Query manages to locate IBD segments quickly with a given cutoff length while allowing mismatched sites. A single query against all UK biobank autosomal chromosomes was completed within 2.76 seconds on average, with the minimum length 7 cM and 700 markers. RaPID-Query achieved a 0.016 false negative rate and a 0.012 false positive rate simultaneously on a chromosome 20 sequencing panel having 86,265 sites. This is comparable to the state-of-the-art IBD detection method TPBWT(out-of-sample) and Hap-IBD. The high-quality IBD segments yielded by RaPID-Query were able to distinguish up to fourth degree of the familial relatedness for a given individual pair, and the area under the receiver operating characteristic curve values are at least 97.28%.AvailabilityThe RaPID-Query program is available at https://github.com/ucfcbb/RaPID-Query.Supplementary informationSupplementary data is available at Bioinformatics online.
| S-EPMC10244210 | biostudies-literature
Project description:BACKGROUND:Right ventricular peak systolic longitudinal strain (RVLS) has emerged as an approach for quantifying right ventricular function in diseases such as pulmonary hypertension and congenital heart disease. A major limitation in applying RVLS is that strain imaging and analysis are proprietary, which may result in systematic differences from vendor to vendor. The goal of this study was to test the reproducibility of right ventricular strain analysis among selected vendor-specific software (VSS) and vendor-independent software (VIS) on images obtained from different ultrasound scanners, as would be common in clinical practice or in a multicenter clinical trial. METHODS:In this prospective, single-center study, 35 patients (5 healthy subjects and 30 with pulmonary hypertension) each underwent two echocardiographic scans, one using GE (Vivid E9) and the other using Philips (iE33) ultrasound systems. Images were analyzed using both VSS and VIS (TomTec) software for determination of RVLS. A repeated-measures analysis of variance was used to assess for any systematic differences among methods, as well as effects of scanner and software and a possible interaction between scanner and software for each strain measurement. RESULTS:Differences for global strains were not statistically significant among VSS packages (P ≥ .05), but some differences were noted between VSS and VIS. Wide variability between regional peak strain measurements was noted, but no systematic differences were found. CONCLUSIONS:Global RVLS values between VSS systems are not significantly different but may differ slightly from VIS. When comparing regional strain between VSS and VIS analyses, there is widespread variability without clear systematic differences.
| S-EPMC6825401 | biostudies-literature
Project description:BackgroundThe use of internet search data has been demonstrated to be effective at predicting influenza incidence. This approach may be more successful for dengue which has large variation in annual incidence and a more distinctive clinical presentation and mode of transmission.MethodsWe gathered freely-available dengue incidence data from Singapore (weekly incidence, 2004-2011) and Bangkok (monthly incidence, 2004-2011). Internet search data for the same period were downloaded from Google Insights for Search. Search terms were chosen to reflect three categories of dengue-related search: nomenclature, signs/symptoms, and treatment. We compared three models to predict incidence: a step-down linear regression, generalized boosted regression, and negative binomial regression. Logistic regression and Support Vector Machine (SVM) models were used to predict a binary outcome defined by whether dengue incidence exceeded a chosen threshold. Incidence prediction models were assessed using r² and Pearson correlation between predicted and observed dengue incidence. Logistic and SVM model performance were assessed by the area under the receiver operating characteristic curve. Models were validated using multiple cross-validation techniques.ResultsThe linear model selected by AIC step-down was found to be superior to other models considered. In Bangkok, the model has an r² = 0.943, and a correlation of 0.869 between fitted and observed. In Singapore, the model has an r² = 0.948, and a correlation of 0.931. In both Singapore and Bangkok, SVM models outperformed logistic regression in predicting periods of high incidence. The AUC for the SVM models using the 75th percentile cutoff is 0.906 in Singapore and 0.960 in Bangkok.ConclusionsInternet search terms predict incidence and periods of large incidence of dengue with high accuracy and may prove useful in areas with underdeveloped surveillance systems. The methods presented here use freely available data and analysis tools and can be readily adapted to other settings.
| S-EPMC3149016 | biostudies-literature
Project description:BackgroundTimely allocation of medical resources for coronavirus disease (COVID-19) requires early detection of regional outbreaks. Internet browsing data may predict case outbreaks in local populations that are yet to be confirmed.ObjectiveWe investigated whether search-engine query patterns can help to predict COVID-19 case rates at the state and metropolitan area levels in the United States.MethodsWe used regional confirmed case data from the New York Times and Google Trends results from 50 states and 166 county-based designated market areas (DMA). We identified search terms whose activity precedes and correlates with confirmed case rates at the national level. We used univariate regression to construct a composite explanatory variable based on best-fitting search queries offset by temporal lags. We measured the raw and z-transformed Pearson correlation and root-mean-square error (RMSE) of the explanatory variable with out-of-sample case rate data at the state and DMA levels.ResultsPredictions were highly correlated with confirmed case rates at the state (mean r=0.69, 95% CI 0.51-0.81; median RMSE 1.27, IQR 1.48) and DMA levels (mean r=0.51, 95% CI 0.39-0.61; median RMSE 4.38, IQR 1.80), using search data available up to 10 days prior to confirmed case rates. They fit case-rate activity in 49 of 50 states and in 103 of 166 DMA at a significance level of .05.ConclusionsIdentifiable patterns in search query activity may help to predict emerging regional outbreaks of COVID-19, although they remain vulnerable to stochastic changes in search intensity.
| S-EPMC7394521 | biostudies-literature
Project description:Background and objectivesPhysicians frequently search bibliographic databases, such as MEDLINE via PubMed, for best evidence for patient care. The objective of this study was to develop and test search filters to help physicians efficiently retrieve literature related to dialysis (hemodialysis or peritoneal dialysis) from all other articles indexed in PubMed, Ovid MEDLINE, and Embase.Design, setting, participants, & measurementsA diagnostic test assessment framework was used to develop and test robust dialysis filters. The reference standard was a manual review of the full texts of 22,992 articles from 39 journals to determine whether each article contained dialysis information. Next, 1,623,728 unique search filters were developed, and their ability to retrieve relevant articles was evaluated.ResultsThe high-performance dialysis filters consisted of up to 65 search terms in combination. These terms included the words "dialy" (truncated), "uremic," "catheters," and "renal transplant wait list." These filters reached peak sensitivities of 98.6% and specificities of 98.5%. The filters' performance remained robust in an independent validation subset of articles.ConclusionsThese empirically derived and validated high-performance search filters should enable physicians to effectively retrieve dialysis information from PubMed, Ovid MEDLINE, and Embase.
| S-EPMC3463205 | biostudies-literature
Project description:BACKGROUND: The Cochrane Highly Sensitive Search Strategy (HSSS), which contains three phases, is widely used to identify Randomized Controlled Trials (RCTs) in MEDLINE. Lefebvre and Clarke suggest that reviewers might consider using four revisions of the HSSS. The objective of this study is to validate these four revisions: combining the free text terms volunteer, crossover, versus, and the Medical Subject Heading CROSS-OVER STUDIES with the top two phases of the HSSS, respectively. METHODS: We replicated the subject search for 61 Cochrane reviews. The included studies of each review that were indexed in MEDLINE were pooled together by review and then combined with the subject search and each of the four proposed search strategies, the top two phases of the HSSS, and all three phases of the HSSS. These retrievals were used to calculate the sensitivity and precision of each of the six search strategies for each review. RESULTS: Across the 61 reviews, the search term versus combined with the top two phases of the HSSS was able to find 3 more included studies than the top two phases of the HSSS alone, or in combination with any of the other proposed search terms, but at the expense of missing 56 relevant articles that would be found if all three phases of the HSSS were used. The estimated time needed to finish a review is 1086 hours for all three phases of the HSSS, 823 hours for the strategy versus, 818 hours for the first two phases of the HSSS or any of the other three proposed strategies. CONCLUSION: This study shows that compared to the first two phases of the HSSS, adding the term versus to the top two phases of the HSSS balances the sensitivity and precision in the reviews studied here to some extent but the differences are very small. It is well known that missing relevant studies may result in bias in systematic reviews. Reviewers need to weigh the trade-offs when selecting the search strategies for identifying RCTs in MEDLINE.
| S-EPMC1488863 | biostudies-other