The relative citation ratio: what is it and why should medical librarians care?

Bibliometrics is becoming increasingly prominent in the world of medical libraries. The number of presentations related to research impact at the Medical Library Association (MLA) annual meeting has been increasing in past years. Medical centers have been using institutional dashboards to track clinical performance for over a decade, and more recently, these institutional dashboards have included measures of academic performance. This commentary reviews current practices and considers the role for a newer metric, the relative citation ratio

Classifying publications from the clinical and translational science award program along the translational research spectrum: a machine learning approach

BACKGROUND: Translational research is a key area of focus of the National Institutes of Health (NIH), as demonstrated by the substantial investment in the Clinical and Translational Science Award (CTSA) program. The goal of the CTSA program is to accelerate the translation of discoveries from the bench to the bedside and into communities. Different classification systems have been used to capture the spectrum of basic to clinical to population health research, with substantial differences in the number of categories and their definitions. Evaluation of the effectiveness of the CTSA program and of translational research in general is hampered by the lack of rigor in these definitions and their application. This study adds rigor to the classification process by creating a checklist to evaluate publications across the translational spectrum and operationalizes these classifications by building machine learning-based text classifiers to categorize these publications. METHODS: Based on collaboratively developed definitions, we created a detailed checklist for categories along the translational spectrum from T0 to T4. We applied the checklist to CTSA-linked publications to construct a set of coded publications for use in training machine learning-based text classifiers to classify publications within these categories. The training sets combined T1/T2 and T3/T4 categories due to low frequency of these publication types compared to the frequency of T0 publications. We then compared classifier performance across different algorithms and feature sets and applied the classifiers to all publications in PubMed indexed to CTSA grants. To validate the algorithm, we manually classified the articles with the top 100 scores from each classifier. RESULTS: The definitions and checklist facilitated classification and resulted in good inter-rater reliability for coding publications for the training set. Very good performance was achieved for the classifiers as represented by the area under the receiver operating curves (AUC), with an AUC of 0.94 for the T0 classifier, 0.84 for T1/T2, and 0.92 for T3/T4. CONCLUSIONS: The combination of definitions agreed upon by five CTSA hubs, a checklist that facilitates more uniform definition interpretation, and algorithms that perform well in classifying publications along the translational spectrum provide a basis for establishing and applying uniform definitions of translational research categories. The classification algorithms allow publication analyses that would not be feasible with manual classification, such as assessing the distribution and trends of publications across the CTSA network and comparing the categories of publications and their citations to assess knowledge transfer across the translational research spectrum

Informationist Support for a Study of the Role of Proteases and Peptides in Cancer Pain

Two supplements were awarded to the New York University Health Sciences Libraries from the National Library of Medicine\u27s informationist grant program. These supplements funded research support in a number of areas, including data management and bioinformatics, two fields that the library had recently begun to explore. As such, the supplements were of particular value to the library as a testing ground for these newer services. This paper will discuss a supplement received in support of a grant from the National Institute of Dental and Craniofacial Research (PI: Brian Schmidt) on the role of proteases and peptides in cancer pain. A number of barriers were preventing the research team from maximizing the efficiency and effectiveness of their work. A critical component of the research was to identify which proteins, from among hundreds identified in collected samples, to include in preclinical testing. This selection involved laborious and prohibitively time-consuming manual searching of the literature on protein function. Additionally, the research team encompassed ten investigators working in two different cities, which led to issues around the sharing and tracking of both data and citations. The supplement outlined three areas in which the informationists would assist the researchers in overcoming these barriers: 1) creating an automated literature searching system for protein function discovery, 2) introducing tools and associated workflows for sharing citations, and 3) introducing tools and workflows for sharing data and specimens

Task shifting interventions for cardiovascular risk reduction in low-income and middle-income countries: A systematic review of randomised controlled trials

Objective: To evaluate evidence from published randomised controlled trials (RCTs) for the use of taskshifting strategies for cardiovascular disease (CVD) risk reduction in low-income and middle-income countries (LMICs). Design: Systematic review of RCTs that utilised a task-shifting strategy in the management of CVD in LMICs. Data Sources: We searched the following databases for relevant RCTs: PubMed from the 1940s, EMBASE from 1974, Global Health from 1910, Ovid Health Star from 1966, Web of Knowledge from 1900, Scopus from 1823, CINAHL from 1937 and RCTs from ClinicalTrials.gov. Eligibility criteria for selecting studies: We focused on RCTs published in English, but without publication year. We included RCTs in which the intervention used task shifting (non-physician healthcare workers involved in prescribing of medications, treatment and/or medical testing) and nonphysician healthcare providers in the management of CV risk factors and diseases (hypertension, diabetes, hyperlipidaemia, stroke, coronary artery disease or heart failure), as well as RCTs that were conducted in LMICs. We excluded studies that are not RCTs. Results: Of the 2771 articles identified, only three met the predefined criteria. All three trials were conducted in practice-based settings among patients with hypertension (2 studies) and diabetes (1 study), with one study also incorporating home visits. The duration of the studies ranged from 3 to 12 months, and the task-shifting strategies included provision of medication prescriptions by nurses, community health workers and pharmacists and telephone follow-up posthospital discharge. Both hypertension studies reported a significant mean blood pressure reduction (2/1 mm Hg and 30/15 mm Hg), and the diabetes trial reported a reduction in the glycated haemoglobin levels of 1.87%. Conclusions: There is a dearth of evidence on the implementation of task-shifting strategies to reduce the burden of CVD in LMICs. Effective task-shifting interventions targeted at reducing the global CVD epidemic in LMICs are urgently needed

Rigor and reproducibility instruction in academic medical libraries

Background: Concerns over scientific reproducibility have grown in recent years, leading the National Institutes of Health (NIH) to require researchers to address these issues in research grant applications. Starting in 2020, training grants were required to provide a plan for educating trainees in rigor and reproducibility. Academic medical centers have responded with different solutions to fill this educational need. As experienced instructors with expertise in topics relating to reproducibility, librarians can play a prominent role in providing trainings, classes, and events to educate investigators and trainees, and bolstering reproducibility in their communities. Case Presentations: This special report summarizes efforts at five institutions to provide education in reproducibility to biomedical and life sciences researchers. Our goal is to expand awareness of the range of approaches in providing reproducibility services in libraries. Conclusions: Reproducibility education by medical librarians can take many forms. These specific programs in reproducibility education build upon libraries’ existing collaborations, with funder mandates providing a major impetus. Collaborator needs shaped the exact type of educational or other reproducibility support and combined with each library’s strengths to yield a diversity of offerings based on capacity and interest. As demand for and complexity of reproducibility education increases due to new institutional and funder mandates, reproducibility education will merit special attention

Collaborations to support data harmonization and discovery in the BRAIN Initiative

The National Institutes of Health (NIH) BRAIN Initiative (https://braininitiative.nih.gov/) funds the development and application of innovative technologies to aid in understanding the human brain. In 2017, it introduced funding mechanisms to support: the development of data archives, standards and tools collaborative research teams studying brain circuit functions' underlying behavior Grants to support the latter required the creation of data science cores, which were tasked with ensuring that the FAIR principles were applied to the data that was collected. NIH created a consortium of the directors of the data science cores for each of the ten grants funded in 2017 and 2018. The purpose of the consortium was to promote collaboration, and the sharing of tools and resources. I am the only librarian among the data science core directors, and I am working - both within my project team and the consortium - to increase the focus on metadata and data discovery. Work is currently ongoing in two areas. The first area involves collaboration with a member of one of the project teams, who had developed a metadata collection tool to capture detailed experimental metadata. We are working together to generalize the existing data model in order to support experimental metadata from all participating labs, and to customize the web interface in order to facilitate efficient metadata collection for labs collecting different types of data. The second area involves exploring the use of a data catalog to improve the discovery of BRAIN Initiative data. This talk will discuss these projects as well as my experiences in integrating with the project team and working within the data science consortium.</p

Training biomedical researchers to effectively collaborate with data scientists

It is not realistic to expect that all biomedical and health sciences researchers will acquire the skills needed to apply data science techniques to their work. However, these researchers are all going to have to function in a research environment where the use of data science techniques is increasingly important. Collaborations between data scientists and researchers with domain expertise afford new opportunities. However, a lack of researcher awareness about data science can result in missed opportunities for collaboration, and differences in perspective and language can result in failed collaborations. Seeing no existing curricula that met the specific need identified, we developed a class to bridge that gap - Data Science for Non-Data Scientists. The class explains the possibilities, techniques, and terminology of data science, as well as conveying its limitations such as issues of interpretation, implementation and bias. This presentation will describe the motivation for developing the class, outline the approach taken and the elements of the class, describe the different settings in which it has been taught within our institution, and detail the outcomes of the class.</p