When Evidence Says No, but Doctors Say Yes

According to Vinay Prasad, an oncologist and one of the authors of the Mayo Clinic Proceedings paper, medicine is quick to adopt practices based on shaky evidence but slow to drop them once they’ve been blown up by solid proof. As a young doctor, Prasad had an experience that left him determined to banish ineffective procedures. He was the medical resident on a team caring for a middle-aged woman with stable chest pain. She underwent a stent procedure and suffered a stroke, resulting in brain damage. Prasad, now at the Oregon Health and Sciences University, still winces slightly when he talks about it. University of Chicago professor and physician Adam Cifu had a similar experience. Cifu had spent several years convincing newly postmenopausal patients to go on hormone therapy for heart health—a treatment that at the millennium accounted for 90 million annual prescriptions—only to then see a well-designed trial show no heart benefit and perhaps even a risk of harm. "I had to basically run back all those decisions with women," he says. "And, boy, that really sticks with you, when you have patients saying, 'But I thought you said this was the right thing.'" So he and Prasad coauthored a 2015 book, Ending Medical Reversal, a call to raise the evidence bar for adopting new medical standards. "We have a culture where we reward discovery; we don’t reward replication," Prasad says, referring to the process of retesting initial scientific findings to make sure they’re valid.

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  • Encouraging Progress toward Reproducibility Reported

    At AAAS 2017, a pair of panel discussions addressed the reproducibility crisis in science, particularly biomedical science, and suggested that it is manageable, provided stakeholders continue to demonstrate a commitment to quality. One panel, led by Leonard P. Freedman, Ph.D., president of Global Biological Standards Institute (GBSI), was comprehensive. It prescribed a range of initiatives.

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  • How to run a lab for reproducible research

    As a principal investigator, how do you run your lab for reproducibility? I submit the following action areas: commitment, transparency and open science, onboarding, collaboration, community and leadership. Make a public commitment to reproducible research—what this means for you could differ from others, but an essential core is common to all. Transparency is an essential value, and embracing open science is the best route to realize it. Onboarding every lab member with a deliberate group “syllabus” for reproducibility sets the expectations high. What is your list of must-read literature on reproducible research? I can share mine with you: my lab members helped to make it. For collaborating efficiently and building community, we take inspiration from the open-source world. We adopt its technology platforms to work on software and to communicate, openly and collaboratively. Key to the open-source culture is to give credit—give lots of credit for every contribution: code, documentation, tests, issue reports! The tools and methods require training, but running a lab for reproducibility is your decision. Start here–>commitment.

    GBSI reports encouraging progress toward improved research reproducibility by year 2020

    One year after the Global Biological Standards Institute (GBSI) issued its Reproducibility2020 challenge and action plan for the biomedical research community, the organization reports encouraging progress toward the goal to significantly improve the quality of preclinical biological research by year 2020. "Reproducibility2020 Report: Progress and Priorities," posted today on bioRxiv, identifies action and impact that has been achieved by the life science research community and outlines priorities going forward. The report is the first comprehensive review of the steps being taken to improve reproducibility since the issue became more widely known in 2012.

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  • New Tools for Content Innovation and Data Sharing: Enhancing Reproducibility and Rigor in Biomechanics Research

    We are currently in one of the most exciting times for science and engineering as we witness unprecedented growth computational and experimental capabilities to generate new data and models. To facilitate data and model sharing, and to enhance reproducibility and rigor in biomechanics research, the Journal of Biomechanics has introduced a number of tools for Content Innovation to allow presentation, sharing, and archiving of methods, models, and data in our articles. The tools include an Interactive Plot Viewer, 3D Geometric Shape and Model Viewer, Virtual Microscope, Interactive MATLAB Figure Viewer, and Audioslides. Authors are highly encouraged to make use of these in upcoming journal submissions.

    Supporting accessibility and reproducibility in language research in the Alveo virtual laboratory

    Reproducibility is an important part of scientific research and studies published in speech and language research usually make some attempt at ensuring that the work reported could be reproduced by other researchers. This paper looks at the current practice in the field relating to the citation and availability of both data and software methods. It is common to use widely available shared datasets in this field which helps to ensure that studies can be reproduced; however a brief survey of recent papers shows a wide range of styles of citation of data only some of which clearly identify the exact data used in the study. Similarly, practices in describing and sharing software artefacts vary considerably from detailed descriptions of algorithms to linked repositories. The Alveo Virtual Laboratory is a web based platform to support research based on collections of text, speech and video. Alveo provides a central repository for language data and provides a set of services for discovery and analysis of data. We argue that some of the features of the Alveo platform may make it easier for researchers to share their data more precisely and cite the exact software tools used to develop published results. Alveo makes use of ideas developed in other areas of science and we discuss these and how they can be applied to speech and language research.

    Challenges of archiving and preserving born-digital news applications

    Born-digital news content is increasingly becoming the format of the first draft of history. Archiving and preserving this history is of paramount importance to the future of scholarly research, but many technical, legal, financial, and logistical challenges stand in the way of these efforts. This is especially true for news applications, or custom-built websites that comprise some of the most sophisticated journalism stories today, such as the “Dollars for Docs” project by ProPublica. Many news applications are standalone pieces of software that query a database, and this significant subset of apps cannot be archived in the same way as text-based news stories, or fully captured by web archiving tools such as Archive-It. As such, they are currently disappearing. This paper will outline the various challenges facing the archiving and preservation of born-digital news applications, as well as outline suggestions for how to approach this important work.

    Facilitating reproducible research by investigating computational metadata

    Computational workflows consist of a series of steps in which data is generated, manipulated, analysed and transformed. Researchers use tools and techniques to capture the provenance associated with the data to aid reproducibility. The metadata collected not only helps in reproducing the computation but also aids in comparing the original and reproduced computations. In this paper, we present an approach, "Why-Diff", to analyse the difference between two related computations by changing the artifacts and how the existing tools "YesWorkflow" and "NoWorkflow" record the changed artifacts.

    Using Docker Containers to Extend Reproducibility Architecture for the NASA Earth Exchange (NEX)

    NASA Earth Exchange (NEX) is a data, supercomputing and knowledge collaboratory that houses NASA satellite, climate and ancillary data where a focused community can come together to address large-scale challenges in Earth sciences. As NEX has been growing into a petabyte-size platform for analysis, experiments and data production, it has been increasingly important to enable users to easily retrace their steps, identify what datasets were produced by which process chains, and give them ability to readily reproduce their results. This can be a tedious and difficult task even for a small project, but is almost impossible on large processing pipelines. We have developed an initial reproducibility and knowledge capture solution for the NEX, however, if users want to move the code to another system, whether it is their home institution cluster, laptop or the cloud, they have to find, build and install all the required dependencies that would run their code. This can be a very tedious and tricky process and is a big impediment to moving code to data and reproducibility outside the original system. The NEX team has tried to assist users who wanted to move their code into OpenNEX on Amazon cloud by creating custom virtual machines with all the software and dependencies installed, but this, while solving some of the issues, creates a new bottleneck that requires the NEX team to be involved with any new request, updates to virtual machines and general maintenance support. In this presentation, we will describe a solution that integrates NEX and Docker to bridge the gap in code-to-data migration. The core of the solution is saemi-automatic conversion of science codes, tools and services that are already tracked and described in the NEX provenance system, to Docker - an open-source Linux container software. Docker is available on most computer platforms, easy to install and capable of seamlessly creating and/or executing any application packaged in the appropriate format. We believe this is an important step towards seamless process deployment in heterogeneous environments that will enhance community access to NASA data and tools in a scalable way, promote software reuse, and improve reproducibility of scientific results.