LitCovid: A Database of Coronavirus Research.

LitCovid is a curated literature hub with more than 60,000 articles about the novel coronavirus and the disease it causes-COVID-19. It is updated daily with scientific information from new PubMed articles that are relevant to COVID-19. Relevant articles are manually assigned to eight broad categories for ease of searching: general, mechanism, transmission, diagnosis, treatment, prevention, case report, and forecasting. Users can filter by location and what clinical trials are happening. As a centralized location curated daily with the most authoritative information on coronavirus and COVID-19, LitCovid is an effective source of credible up-to-date research.

Creating enriched training sets of eligible studies for large systematic reviews: the utility of PubMed's Best Match algorithm.

INTRODUCTION: Solutions like crowd screening and machine learning can assist systematic reviewers with heavy screening burdens but require training sets containing a mix of eligible and ineligible studies. This study explores using PubMed's Best Match algorithm to create small training sets containing at least five relevant studies. METHODS: Six systematic reviews were examined retrospectively. MEDLINE searches were converted and run in PubMed. The ranking of included studies was studied under both Best Match and Most Recent sort conditions. RESULTS: Retrieval sizes for the systematic reviews ranged from 151 to 5,406 records and the numbers of relevant records ranged from 8 to 763. The median ranking of relevant records was higher in Best Match for all six reviews, when compared with Most Recent sort. Best Match placed a total of thirty relevant records in the first fifty, at least one for each systematic review. Most Recent sorting placed only ten relevant records in the first fifty. Best Match sorting outperformed Most Recent in all cases and placed five or more relevant records in the first fifty in three of six cases. DISCUSSION: Using a predetermined set size such as fifty may not provide enough true positives for an effective systematic review training set. However, screening PubMed records ranked by Best Match and continuing until the desired number of true positives are identified is efficient and effective. CONCLUSIONS: The Best Match sort in PubMed improves the ranking and increases the proportion of relevant records in the first fifty records relative to sorting by recency.

PubMed 2.0.

After years of strategic planning, the National Library of Medicine has introduced an updated and redesigned version of its PubMed health sciences research website. The new website features a more modern and responsive interface, especially on mobile devices. Tools and features have been relocated to make them more intuitive for new users. While not without some turbulence and slight discomfort for long-time users adjusting to the modernized interface and search engine, the new version of the PubMed website introduced in 2020 succeeds in the website's time-honored task of collecting and making freely accessible high-quality health sciences information and resources.

How Does Pretesting for PubMed Knowledge Spark Student Learning?

Predoctoral students enter dental school with varying skill levels for searching biomedical databases and a tendency to overestimate their abilities. Accordingly, PubMed instruction is embedded within a required dental course and includes a graded component. This article describes a pretest/intervention/posttest developed for the PubMed session. The expectation for this new assessment was that motivation to learn PubMed would increase during the intervention if pretesting objectively showed students the difference between their self-perceived versus actual PubMed abilities. The goals were to help students better self-assess their genuine searching abilities, spark learning during the instruction session, and elicit measurable improvement in skills.

Automatic generation of investigator bibliographies for institutional research networking systems.

OBJECTIVE: Publications are a key data source for investigator profiles and research networking systems. We developed ReCiter, an algorithm that automatically extracts bibliographies from PubMed using institutional information about the target investigators. METHODS: ReCiter executes a broad query against PubMed, groups the results into clusters that appear to constitute distinct author identities and selects the cluster that best matches the target investigator. Using information about investigators from one of our institutions, we compared ReCiter results to queries based on author name and institution and to citations extracted manually from the Scopus database. Five judges created a gold standard using citations of a random sample of 200 investigators. RESULTS: About half of the 10,471 potential investigators had no matching citations in PubMed, and about 45% had fewer than 70 citations. Interrater agreement (Fleiss' kappa) for the gold standard was 0.81. Scopus achieved the best recall (sensitivity) of 0.81, while name-based queries had 0.78 and ReCiter had 0.69. ReCiter attained the best precision (positive predictive value) of 0.93 while Scopus had 0.85 and name-based queries had 0.31. DISCUSSION: ReCiter accesses the most current citation data, uses limited computational resources and minimizes manual entry by investigators. Generation of bibliographies using named-based queries will not yield high accuracy. Proprietary databases can perform well but requite manual effort. Automated generation with higher recall is possible but requires additional knowledge about investigators.

Bosnian and Herzegovinian medical scientists in PubMed database.

In this paper it is shortly presented PubMed as one of the most important on-line databases of the scientific biomedical literature. Also, the author has analyzed the most cited authors, professors of the medical faculties in Bosnia and Herzegovina, from the published papers in the biomedical journals abstracted and indexed in PubMed.

Impact of PubMed search filters on the retrieval of evidence by physicians.

BACKGROUND: Physicians face challenges when searching PubMed for research evidence, and they may miss relevant articles while retrieving too many nonrelevant articles. We investigated whether the use of search filters in PubMed improves searching by physicians. METHODS: We asked a random sample of Canadian nephrologists to answer unique clinical questions derived from 100 systematic reviews of renal therapy. Physicians provided the search terms that they would type into PubMed to locate articles to answer these questions. We entered the physician-provided search terms into PubMed and applied two types of search filters alone or in combination: a methods-based filter designed to identify high-quality studies about treatment (clinical queries "therapy") and a topic-based filter designed to identify studies with renal content. We evaluated the comprehensiveness (proportion of relevant articles found) and efficiency (ratio of relevant to nonrelevant articles) of the filtered and nonfiltered searches. Primary studies included in the systematic reviews served as the reference standard for relevant articles. RESULTS: The average physician-provided search terms retrieved 46% of the relevant articles, while 6% of the retrieved articles were relevant (corrected) (the ratio of relevant to nonrelevant articles was 1:16). The use of both filters together produced a marked improvement in efficiency, resulting in a ratio of relevant to nonrelevant articles of 1:5 (16 percentage point improvement; 99% confidence interval 9% to 22%; p < 0.003) with no substantive change in comprehensiveness (44% of relevant articles found; p = 0.55). INTERPRETATION: The use of PubMed search filters improves the efficiency of physician searches. Improved search performance may enhance the transfer of research into practice and improve patient care.

[Knowledgebase as a tool for monitoring post-genomic medico-biological research].

On-going molecular biological researches are characterized by exponential increase in the amount of experimental data which dictates the necessity to develop relevant information technologies. The knowledge-based approach appears to be a most promising and flexible tool for analytical processing biomedical information that allows new relationships between study objects to be established. In this article we analyze the work of researchers based at the Institute of Biomedical Chemistry with the materials of the PubMed biomedical library. A list of publication IDs for a half-year period has been compiled to elucidate individual profiles of research activities. Statistical analysis of medical subject headings (MeSH) reveals typical profiles of research of interest for separate divisions of the Institute. The proposed approach may be recommended as a means for improving the efficiency and coordination of biomedical research.

NCBI Genome Workbench: Desktop Software for Comparative Genomics, Visualization, and GenBank Data Submission.

The book chapter introduces the National Center for Biotechnology Information (NCBI) Genome Workbench, a desktop GUI software package to manipulate and visualize complex molecular biology models provided in many data formats. Genome Workbench integrates graphical views and computational tools in a single package to facilitate discoveries. In this chapter we provide a step-by-step protocol guidance on how to do comparative analysis of sequences using NCBI BLAST and multiple sequence alignment algorithms, build phylogenetic trees, and use graphical views for sequences, alignments, and trees to validate the findings. The software package can be used to prepare high-quality whole genome submissions to NCBI. The software package is user-friendly and includes validation and editing tools to fix errors as part of preparing the submission.

"There is nothing like looking, if you want to find something" - asking questions and searching for answers - the evidence based approach.

Evidence based medicine (EBM) consists of four separate processes. Asking a clinical question Locating the evidence Appraising and synthesizing the evidence Applying the evidence Most people are familiar with the third step but not so familiar with the first two. Well-designed clinical questions can help clarify in a clinicians mind exactly what information is being sought and also help with the search. This clinical question can be framed around the PICO format. With a well-structured PICO information can then be located in a timely manner using the concept of Haynes's 5 S's and a range of websites including trial registers and meta-search engines.

Answers to questions posed during daily patient care are more likely to be answered by UpToDate than PubMed.

BACKGROUND: UpToDate and PubMed are popular sources for medical information. Data regarding the efficiency of PubMed and UpToDate in daily medical care are lacking. OBJECTIVE: The purpose of this observational study was to describe the percentage of answers retrieved by these information sources, comparing search results with regard to different medical topics and the time spent searching for an answer. METHODS: A total of 40 residents and 30 internists in internal medicine working in an academic medical center searched PubMed and UpToDate using an observation portal during daily medical care. The information source used for searching and the time needed to find an answer to the question were recorded by the portal. Information was provided by searchers regarding the topic of the question, the situation that triggered the question, and whether an answer was found. RESULTS: We analyzed 1305 patient-related questions sent to PubMed and/or UpToDate between October 1, 2005 and March 31, 2007 using our portal. A complete answer was found in 594/1125 (53%) questions sent to PubMed or UpToDate. A partial or full answer was obtained in 729/883 (83%) UpToDate searches and 152/242 (63%) PubMed searches (P < .001). UpToDate answered more questions than PubMed on all major medical topics, but a significant difference was detected only when the question was related to etiology (P < .001) or therapy (P = .002). Time to answer was 241 seconds (SD 24) for UpToDate and 291 seconds (SD 7) for PubMed. CONCLUSIONS: Specialists and residents in internal medicine generally use less than 5 minutes to answer patient-related questions in daily care. More questions are answered using UpToDate than PubMed on all major medical topics.

Extending PubMed searches to ClinicalTrials.gov through a machine learning approach for systematic reviews.

OBJECTIVES: Despite their essential role in collecting and organizing published medical literature, indexed search engines are unable to cover all relevant knowledge. Hence, current literature recommends the inclusion of clinical trial registries in systematic reviews (SRs). This study aims to provide an automated approach to extend a search on PubMed to the ClinicalTrials.gov database, relying on text mining and machine learning techniques. STUDY DESIGN AND SETTING: The procedure starts from a literature search on PubMed. Next, it considers the training of a classifier that can identify documents with a comparable word characterization in the ClinicalTrials.gov clinical trial repository. Fourteen SRs, covering a broad range of health conditions, are used as case studies for external validation. A cross-validated support-vector machine (SVM) model was used as the classifier. RESULTS: The sensitivity was 100% in all SRs except one (87.5%), and the specificity ranged from 97.2% to 99.9%. The ability of the instrument to distinguish on-topic from off-topic articles ranged from an area under the receiver operator characteristic curve of 93.4% to 99.9%. CONCLUSION: The proposed machine learning instrument has the potential to help researchers identify relevant studies in the SR process by reducing workload, without losing sensitivity and at a small price in terms of specificity.

The identification of clinically important elements within medical journal abstracts: Patient-Population-Problem, Exposure-Intervention, Comparison, Outcome, Duration and Results (PECODR).

BACKGROUND: Information retrieval in primary care is becoming more difficult as the volume of medical information held in electronic databases expands. The lexical structure of this information might permit automatic indexing and improved retrieval. OBJECTIVE: To determine the possibility of identifying the key elements of clinical studies, namely Patient-Population-Problem, Exposure-Intervention, Comparison, Outcome, Duration and Results (PECODR), from abstracts of medical journals. METHODS: We used a convenience sample of 20 synopses from the journal Evidence-Based Medicine (EBM) and their matching original journal article abstracts obtained from PubMed. Three independent primary care professionals identified PECODR-related extracts of text. Rules were developed to define each PECODR element and the selection process of characters, words, phrases and sentences. From the extracts of text related to PECODR elements, potential lexical patterns that might help identify those elements were proposed and assessed using NVivo software. RESULTS: A total of 835 PECODR-related text extracts containing 41,263 individual text characters were identified from 20 EBM journal synopses. There were 759 extracts in the corresponding PubMed abstracts containing 31,947 characters. PECODR elements were found in nearly all abstracts and synopses with the exception of duration. There was agreement on 86.6% of the extracts from the 20 EBM synopses and 85.0% on the corresponding PubMed abstracts. After consensus this rose to 98.4% and 96.9% respectively. We found potential text patterns in the Comparison, Outcome and Results elements of both EBM synopses and PubMed abstracts. Some phrases and words are used frequently and are specific for these elements in both synopses and abstracts. CONCLUSIONS: Results suggest a PECODR-related structure exists in medical abstracts and that there might be lexical patterns specific to these elements. More sophisticated computer-assisted lexical-semantic analysis might refine these results, and pave the way to automating PECODR indexing, and improve information retrieval in primary care.

[Role of academic social networks in disseminating the scientific production of researchers in biology/medicine: the example of ResearchGate]. / Réseaux sociaux académiques et diffusion de la production scientifique des chercheurs en biologie/médecine - L'exemple de ResearchGate.

Over time, academic social networks are more and more used by researchers, especially thanks to the possibilities of sharing articles they offer. The objective of the present study was to evaluate the proportion, the typology (pre-print, post-print author/publisher) and the legality of the full-text publications deposited by researchers on ResearchGate which is widely used by the medical and biological community, using a sample of 1,500 randomly selected articles in PubMed and published between 2013 and 2015. To compare, the access to the full-text of the 1500 articles via PubMed and PubMed Central has been assessed, putting into evidence the important role ResearchGate plays for providing full-texts of articles in biology/medicine. It also puts academic social networks into perspective in relation to open-access repositories and open access.

[MEDLINE and PubMed 2006. Presentation and instructions for use]. / MEDLINE et PubMed 2006. Présentation et mode d'emploi.

The aim of this paper is to provide a practical kit in French for bibliographic retrieval using MEDLINE on PubMed new version, a World Wide Web retrieval service. We explain screen by screen how this bibliographic research tool works and try to make it easy to use to every single French-speaking end-user.