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  1. Article ; Online: Sedimentary legacy and the disturbing recurrence of the human in long-term ecological research.

    Hirsch, Shana Lee / Ribes, David / Inman, Sarah

    Social studies of science

    2022  Volume 52, Issue 4, Page(s) 561–580

    Abstract: Even as new elements of a research infrastructure are added, older parts continue to exert persistent and consequential influence. We introduce the concept of sedimentary legacy to describe the relationship between infrastructure and research objects. ... ...

    Abstract Even as new elements of a research infrastructure are added, older parts continue to exert persistent and consequential influence. We introduce the concept of sedimentary legacy to describe the relationship between infrastructure and research objects. Contrary to common accounts of legacy infrastructure that underscore lock-in, static, or constraining outcomes, sedimentary legacy emphasizes how researchers adapt infrastructure to support the investigation of new research objects, even while operating under constraining legacies. To illustrate the implications of sedimentary legacy, we track shifting objects of investigation across the history of the Long-Term Ecological Research (LTER) Network, focusing especially on recurrent ecological investigations of 'human disturbance' as researchers shift to study socioecological objects. We examine the relationship between scientific objects and the resources collected and preserved to render such objects tractable to scientific investigations, and show how the resources of a long-term research infrastructure support the assembly of certain objects of investigation, even while foreclosing others.
    Language English
    Publishing date 2022-06-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1482712-8
    ISSN 1460-3659 ; 0306-3127
    ISSN (online) 1460-3659
    ISSN 0306-3127
    DOI 10.1177/03063127221101171
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Predicting high-risk opioid prescriptions before they are given.

    Hastings, Justine S / Howison, Mark / Inman, Sarah E

    Proceedings of the National Academy of Sciences of the United States of America

    2020  Volume 117, Issue 4, Page(s) 1917–1923

    Abstract: Misuse of prescription opioids is a leading cause of premature death in the United States. We use state government administrative data and machine learning methods to examine whether the risk of future opioid dependence, abuse, or poisoning can be ... ...

    Abstract Misuse of prescription opioids is a leading cause of premature death in the United States. We use state government administrative data and machine learning methods to examine whether the risk of future opioid dependence, abuse, or poisoning can be predicted in advance of an initial opioid prescription. Our models accurately predict these outcomes and identify particular prior nonopioid prescriptions, medical history, incarceration, and demographics as strong predictors. Using our estimates, we simulate a hypothetical policy which restricts new opioid prescriptions to only those with low predicted risk. The policy's potential benefits likely outweigh costs across demographic subgroups, even for lenient definitions of "high risk." Our findings suggest new avenues for prevention using state administrative data, which could aid providers in making better, data-informed decisions when weighing the medical benefits of opioid therapy against the risks.
    MeSH term(s) Aged ; Algorithms ; Analgesics, Opioid/therapeutic use ; Drug Prescriptions/standards ; Female ; Humans ; Machine Learning ; Male ; Middle Aged ; Opioid-Related Disorders/drug therapy ; Opioid-Related Disorders/epidemiology ; Practice Patterns, Physicians'/standards ; Predictive Value of Tests ; Prescription Drug Misuse/prevention & control ; Rhode Island/epidemiology ; Risk Assessment/methods
    Chemical Substances Analgesics, Opioid
    Language English
    Publishing date 2020-01-14
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.1905355117
    Database MEDical Literature Analysis and Retrieval System OnLINE

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