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1. Inferring Which Medical TreatmentsWork from Reports of Clinical Trials

   Lehman, Eric ; DeYoung, Jay ; Barzilay, Regina ; Wallace, Byron C. ( January 2019 , Annual Conference of the North American Chapter of the Association for Computational Linguistics)

   How do we know if a particular medical treatment actually works? Ideally one would consult all available evidence from relevant clinical trials. Unfortunately, such results are primarily disseminated in natural language scientific articles, imposing substantial burden on those trying to make sense of them. In this paper, we present a new task and corpus for making this unstructured evidence actionable. The task entails inferring reported findings from a full-text article describing a randomized controlled trial (RCT) with respect to a given intervention, comparator, and outcome of interest, e.g., inferring if an article provides evidence supporting the use of aspirin to reduce risk of stroke, as compared to placebo. We present a new corpus for this task comprising 10,000+ prompts coupled with fulltext articles describing RCTs. Results using a suite of models — ranging from heuristic (rule-based) approaches to attentive neural architectures — demonstrate the difficulty of the task, which we believe largely owes to the lengthy, technical input texts. To facilitate further work on this important, challenging problem we make the corpus, documentation, a website and leaderboard, and code for baselines and evaluation available at http: //evidence-inference.ebm-nlp.com/. 

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2. Generalizability and Transportability of the National Lung Screening Trial Data: Extending Trial Results to Different Populations

   https://doi.org/10.1158/1055-9965.EPI-21-0585  

   Inoue, Kosuke ; Hsu, William ; Arah, Onyebuchi A. ; Prosper, Ashley E. ; Aberle, Denise R. ; Bui, Alex A. T. ( September 2021 , Cancer Epidemiology, Biomarkers & Prevention)

   Randomized controlled trials (RCT) play a central role in evidence-based healthcare. However, the clinical and policy implications of implementing RCTs in clinical practice are difficult to predict as the studied population is often different from the target population where results are being applied. This study illustrates the concepts of generalizability and transportability, demonstrating their utility in interpreting results from the National Lung Screening Trial (NLST).

   Using inverse-odds weighting, we demonstrate how generalizability and transportability techniques can be used to extrapolate treatment effect from (i) a subset of NLST to the entire NLST population and from (ii) the entire NLST to different target populations.

   Our generalizability analysis revealed that lung cancer mortality reduction by LDCT screening across the entire NLST [16% (95% confidence interval [CI]: 4–24)] could have been estimated using a smaller subset of NLST participants. Using transportability analysis, we showed that populations with a higher prevalence of females and current smokers had a greater reduction in lung cancer mortality with LDCT screening [e.g., 27% (95% CI, 11–37) for the population with 80% females and 80% current smokers] than those with lower prevalence of females and current smokers.

   This article illustrates how generalizability and transportability methods extend estimation of RCTs' utility beyond trial participants, to external populations of interest, including those that more closely mirror real-world populations.

   Generalizability and transportability approaches can be used to quantify treatment effects for populations of interest, which may be used to design future trials or adjust lung cancer screening eligibility criteria.

    

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3. Internet-Based Individualized Cognitive Behavioral Therapy for Shift Work Sleep Disorder Empowered by Well-Being Prediction: Protocol for a Pilot Study

   https://doi.org/10.2196/24799  

   Ito-Masui, Asami ; Kawamoto, Eiji ; Sakamoto, Ryota ; Yu, Han ; Sano, Akane ; Motomura, Eishi ; Tanii, Hisashi ; Sakano, Shoko ; Esumi, Ryo ; Imai, Hiroshi ; et al ( January 2021 , JMIR Research Protocols)

   null (Ed.)

   Background Shift work sleep disorders (SWSDs) are associated with the high turnover rates of nurses, and are considered a major medical safety issue. However, initial management can be hampered by insufficient awareness. In recent years, it has become possible to visualize, collect, and analyze the work-life balance of health care workers with irregular sleeping and working habits using wearable sensors that can continuously monitor biometric data under real-life settings. In addition, internet-based cognitive behavioral therapy for psychiatric disorders has been shown to be effective. Application of wearable sensors and machine learning may potentially enhance the beneficial effects of internet-based cognitive behavioral therapy. Objective In this study, we aim to develop and evaluate the effect of a new internet-based cognitive behavioral therapy for SWSD (iCBTS). This system includes current methods such as medical sleep advice, as well as machine learning well-being prediction to improve the sleep durations of shift workers and prevent declines in their well-being. Methods This study consists of two phases: (1) preliminary data collection and machine learning for well-being prediction; (2) intervention and evaluation of iCBTS for SWSD. Shift workers in the intensive care unit at Mie University Hospital will wear a wearable sensor that collects biometric data and answer daily questionnaires regarding their well-being. They will subsequently be provided with an iCBTS app for 4 weeks. Sleep and well-being measurements between baseline and the intervention period will be compared. Results Recruitment for phase 1 ended in October 2019. Recruitment for phase 2 has started in October 2020. Preliminary results are expected to be available by summer 2021. Conclusions iCBTS empowered with well-being prediction is expected to improve the sleep durations of shift workers, thereby enhancing their overall well-being. Findings of this study will reveal the potential of this system for improving sleep disorders among shift workers. Trial Registration UMIN Clinical Trials Registry UMIN000036122 (phase 1), UMIN000040547 (phase 2); https://tinyurl.com/dkfmmmje, https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046284 International Registered Report Identifier (IRRID) DERR1-10.2196/24799 

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4. The Salt Controversy Systematic Review Reports and Primary Study Reports Network Dataset

   https://doi.org/10.13012/B2IDB-6128763_V2  

   Fu, Yuanxi ; Hsiao, Tzu-Kun ; Joshi, Manasi Ballal ( January 2022 , University of Illinois at Urbana-Champaign)

   The salt controversy is the public health debate about whether a population-level salt reduction is beneficial. This dataset covers 82 publications--14 systematic review reports (SRRs) and 68 primary study reports (PSRs)--addressing the effect of sodium intake on cerebrocardiovascular disease or mortality. These present a snapshot of the status of the salt controversy as of September 2014 according to previous work by epidemiologists: The reports and their opinion classification (for, against, and inconclusive) were from Trinquart et al. (2016) (Trinquart, L., Johns, D. M., & Galea, S. (2016). Why do we think we know what we know? A metaknowledge analysis of the salt controversy. International Journal of Epidemiology, 45(1), 251–260. https://doi.org/10.1093/ije/dyv184 ), which collected 68 PSRs, 14 SRRs, 11 clinical guideline reports, and 176 comments, letters, or narrative reviews. Note that our dataset covers only the 68 PSRs and 14 SRRs from Trinquart et al. 2016, not the other types of publications, and it adds additional information noted below. This dataset can be used to construct the inclusion network and the co-author network of the 14 SRRs and 68 PSRs. A PSR is "included" in an SRR if it is considered in the SRR's evidence synthesis. Each included PSR is cited in the SRR, but not all references cited in an SRR are included in the evidence synthesis or PSRs. Based on which PSRs are included in which SRRs, we can construct the inclusion network. The inclusion network is a bipartite network with two types of nodes: one type represents SRRs, and the other represents PSRs. In an inclusion network, if an SRR includes a PSR, there is a directed edge from the SRR to the PSR. The attribute file (report_list.csv) includes attributes of the 82 reports, and the edge list file (inclusion_net_edges.csv) contains the edge list of the inclusion network. Notably, 11 PSRs have never been included in any SRR in the dataset. They are unused PSRs. If visualized with the inclusion network, they will appear as isolated nodes. We used a custom-made workflow (Fu, Y. (2022). Scopus author info tool (1.0.1) [Python]. https://github.com/infoqualitylab/Scopus_author_info_collection ) that uses the Scopus API and manual work to extract and disambiguate authorship information for the 82 reports. The author information file (salt_cont_author.csv) is the product of this workflow and can be used to compute the co-author network of the 82 reports. We also provide several other files in this dataset. We collected inclusion criteria (the criteria that make a PSR eligible to be included in an SRR) and recorded them in the file systematic_review_inclusion_criteria.csv. We provide a file (potential_inclusion_link.csv) recording whether a given PSR had been published as of the search date of a given SRR, which makes the PSR potentially eligible for inclusion in the SRR. We also provide a bibliography of the 82 publications (supplementary_reference_list.pdf). Lastly, we discovered minor discrepancies between the inclusion relationships identified by Trinquart et al. (2016) and by us. Therefore, we prepared an additional edge list (inclusion_net_edges_trinquart.csv) to preserve the inclusion relationships identified by Trinquart et al. (2016). UPDATES IN THIS VERSION COMPARED TO V1 (Fu, Yuanxi; Hsiao, Tzu-Kun; Joshi, Manasi Ballal (2022): The Salt Controversy Systematic Review Reports and Primary Study Reports Network Dataset. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-6128763_V1) - We added two new columns in salt_cont_author.csv, "author_id_scopus" and "author_id_mannual" to indicate which author ids were from Scopus and which were assigned by us. - We corrected a few mistakes in "last_search_year," "last_search_month," and "last_search_day" column in systematic_review_inclusion_criteria.csv. - We systematically adjusted the information related to report #12 in report_list.csv, systematic_review_inclusion_criteria.csv, supplementary_reference_list.pdf, salt_cont_author.csv, and inclusion_net_edges.csv to reflect information found in Adler 2014 (Adler, A. J., Taylor, F., Martin, N., Gottlieb, S., Taylor, R. S., & Ebrahim, S. (2014). Reduced dietary salt for the prevention of cardiovascular disease. Cochrane Database of Systematic Reviews, 12. https://doi.org/10.1002/14651858.CD009217.pub3). See our explaination in section "Explanations about report #12". - We sorted the salt_cont_author.csv file by "author_id," not by "ID" (the id of the report). 

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5. Leveraging infectious disease models to interpret randomized controlled trials: Controlling enteric pathogen transmission through water, sanitation, and hygiene interventions

   https://doi.org/10.1371/journal.pcbi.1010748  

   Brouwer, Andrew F. ; Eisenberg, Marisa C. ; Bakker, Kevin M. ; Boerger, Savannah N. ; Zahid, Mondal H. ; Freeman, Matthew C. ; Eisenberg, Joseph N. ( December 2022 , PLOS Computational Biology)

   Lau, Eric HY (Ed.)

   Randomized controlled trials (RCTs) evaluate hypotheses in specific contexts and are often considered the gold standard of evidence for infectious disease interventions, but their results cannot immediately generalize to other contexts (e.g., different populations, interventions, or disease burdens). Mechanistic models are one approach to generalizing findings between contexts, but infectious disease transmission models (IDTMs) are not immediately suited for analyzing RCTs, since they often rely on time-series surveillance data. We developed an IDTM framework to explain relative risk outcomes of an infectious disease RCT and applied it to a water, sanitation, and hygiene (WASH) RCT. This model can generalize the RCT results to other contexts and conditions. We developed this compartmental IDTM framework to account for key WASH RCT factors: i) transmission across multiple environmental pathways, ii) multiple interventions applied individually and in combination, iii) adherence to interventions or preexisting conditions, and iv) the impact of individuals not enrolled in the study. We employed a hybrid sampling and estimation framework to obtain posterior estimates of mechanistic parameter sets consistent with empirical outcomes. We illustrated our model using WASH Benefits Bangladesh RCT data (n = 17,187). Our model reproduced reported diarrheal prevalence in this RCT. The baseline estimate of the basic reproduction number R 0 for the control arm (1.10, 95% CrI: 1.07, 1.16) corresponded to an endemic prevalence of 9.5% (95% CrI: 7.4, 13.7%) in the absence of interventions or preexisting WASH conditions. No single pathway was likely able to sustain transmission: pathway-specific R 0 s for water, fomites, and all other pathways were 0.42 (95% CrI: 0.03, 0.97), 0.20 (95% CrI: 0.02, 0.59), and 0.48 (95% CrI: 0.02, 0.94), respectively. An IDTM approach to evaluating RCTs can complement RCT analysis by providing a rigorous framework for generating data-driven hypotheses that explain trial findings, particularly unexpected null results, opening up existing data to deeper epidemiological understanding. 

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