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Relevant bibliographies by topics / Medical statistics

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Academic literature on the topic 'Medical statistics'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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Journal articles on the topic "Medical statistics"

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Dobson, A. J. "Medical statistics." Medical Journal of Australia 150, no. 1 (January 1989): 44–45. http://dx.doi.org/10.5694/j.1326-5377.1989.tb136327.x.

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Fukaya, Takashi. "Medical Statistics." Equilibrium Research 61, no. 4 (2002): 221–23. http://dx.doi.org/10.3757/jser.61.221.

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Dai, Feng. "Medical Statistics." Anesthesia & Analgesia 125, no. 5 (November 2017): 1814. http://dx.doi.org/10.1213/ane.0000000000002420.

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Betts, Anne. "Medical statistics." Nurse Education Today 10, no. 6 (December 1990): 474. http://dx.doi.org/10.1016/0260-6917(90)90124-9.

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Alderson, Michael. "Medical statistics: overview." Aslib Proceedings 38, no. 6/7 (June 1986): 183–86. http://dx.doi.org/10.1108/eb051012.

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Rees, D. G., and R. F. Mould. "Introductory Medical Statistics." Statistician 38, no. 2 (1989): 142. http://dx.doi.org/10.2307/2348325.

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Morita, Satoshi. "MEDICAL STATISTICS FOR SURGEONS." Nihon Rinsho Geka Gakkai Zasshi (Journal of Japan Surgical Association) 78, no. 7 (2017): 1688–90. http://dx.doi.org/10.3919/jjsa.78.1688.

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Caprioli, Joseph. "Medical Uses of Statistics." Ophthalmic Surgery, Lasers and Imaging Retina 19, no. 2 (February 1988): 146. http://dx.doi.org/10.3928/1542-8877-19880201-23.

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Armitage, Peter. "Biometry and Medical Statistics." Biometrics 41, no. 4 (December 1985): 823. http://dx.doi.org/10.2307/2530956.

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Cornell, Richard G., Fredric M. Wolf, John C. Bailar, and Frederick Mosteller. "Medical Uses of Statistics." Journal of the American Statistical Association 84, no. 406 (June 1989): 629. http://dx.doi.org/10.2307/2289981.

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Dissertations / Theses on the topic "Medical statistics"

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Wong, Sik-kwan Francis. "Outcome of a web-based statistic laboratory for teaching and learning of medical statistics." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43251687.

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Raner, Max. "On logistic regression and a medical application." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420680.

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Hua, Hairui. "Survival modelling in mathematical and medical statistics." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5808/.

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An essential aspect of survival analysis is the estimation and prediction of survival probabilities for individuals. For this purpose, mathematical modelling of the hazard rate function is a fundamental issue. This thesis focuses on the novel estimation and application of hazard rate functions in mathematical and medical research. In mathematical research we focus on the development of a semiparametric kernel-based estimate of hazard rate function and a L\(_1\) error optimal kernel hazard rate estimate. In medical research we concentrate on the development and validation of survival models using individual participant data from multiple studies. We also consider how to fit survival models that predict individual response to treatment effectiveness, given IPD from multiple trials.

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Buchan, Iain Edward. "The development of a statistical computer software resource for medical research." Thesis, University of Liverpool, 2000. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:71360.

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Medical research is often weakened by poor statistical practice, and inappropriate use of statistical computer software is part of this problem. The statistical knowledge that medical researchers require has traditionally been gained in both dedicated and ad hoc learning time, often separate from the research processes in which the statistical methods are applied. Computer software, however, can be written to flexibly support statistical practice. The work of this thesis was to explore the possibility of, and if possible, to create, a resource supporting medical researchers in statistical knowledge and calculation at the point of need. The work was carried out over eleven years, and was directed towards the medical research community in general. Statistical and Software Engineering methods were used to produce a unified statistical computational and knowledge support resource. Mathematically and computationally robust approaches to statistical methods were continually sought from current literature. The type of evaluation undertaken was formative; this included monitoring uptake of the software and feedback from its users, comparisons with other software, reviews in peer reviewed publications, and testing of results against classical and reference data. Large-scale opportunistic feedback from users of this resource was employed in its continuous improvement. The software resulting from the work of this thesis is provided herein as supportive evidence. Results of applying the software to classical reference data are shown in the written thesis. The scope and presentation of statistical methods are considered in a comparison of the software with common statistical software resources. This comparison showed that the software written for this thesis more closely matched statistical methods commonly used in medical research, and contained more statistical knowledge support materials. Up to October 31st 2000, uptake of the software was recorded for 5621 separate instances by individuals or institutions. The development has been self-sustaining. Medical researchers need to have sufficient statistical understanding, just as statistical researchers need to sufficiently understand the nature of data. Statistical software tools may damage statistical practice if they distract attention from statistical goals and tasks, onto the tools themselves. The work of this thesis provides a practical computing framework supporting statistical knowledge and calculation in medical research. This work has shown that sustainable software can be engineered to improve statistical appreciation and practice in ways that are beyond the reach of traditional medical statistical education.

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黃式鈞 and Sik-kwan Francis Wong. "Outcome of a web-based statistic laboratory for teaching and learning of medical statistics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43251687.

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Sonesson, Christian. "On statistical surveillance issues of optimality and medical applications /." Göteborg, Sweden : Stockholm : Statistical Research Unit, Göteborg University ; Almqvist & Wiksell International, 2003. http://catalog.hathitrust.org/api/volumes/oclc/53500706.html.

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Coupal, Louis. "The EM algorithm : an overview with applications to medical data." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=56644.

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Owing to their complex design and use of live subjects as experimental units, missing or incomplete data is common place in medical experiments. The great increase in difficulty of maximum likelihood based analysis of incomplete data experiments compared to a similar complete data analysis encourages many medical researchers to ignore cases with missing data in favour of performing a "complete" cases analysis.
The expectation maximization algorithm (EM for short) is often an easily implemented algorithm that provides estimates of parameters in models with missing data. The EM algorithm unifies the theory of maximum likelihood estimation in the context of "missing" data. The general problem of missing data also includes structurally unobservable quantities such as parameters, hyperparameters and latent variables. The nature of its defining steps, the expectation or E-step and the maximization or M-step, gives the user intuitive understanding of the maximization process.
In this Thesis, the EM algorithm is first illustrated through an example borrowed from the field of genetics. The theory of the EM algorithm is formally developed and the special case of exponential families is considered. Issues concerning convergence and inference are discussed. Many examples taken from the medical literature serve to highlight the method's broad spectrum of application in both missing data and unobservable parameter problems.

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Walker, Stephen Graham. "Bayesian parametric and nonparametric methods with applications in medical statistics." Thesis, Imperial College London, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307519.

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Tom, Brian Dermot Ming. "Modelling event-history data in the context of medical statistics." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624771.

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Joubert, Georgina. "Variable selection in logistic regression, with special application to medical data." Master's thesis, University of Cape Town, 1994. http://hdl.handle.net/11427/17006.

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Bibliography: pages 121-126.
In this thesis, the various methods of variable selection which have been proposed in the statistical, epidemiological and medical literature for prediction and estimation problems in logistic regression will be described. The procedures will be applied to medical data sets. On the basis of the literature review as well as the applications to examples, strengths and weaknesses of the approaches will be identified. The procedures will be compared on the basis of the results obtained, their appropriateness for the specific aim of the analysis, and demands they place on the analyst and researcher, intellectually and computationally. In particular, certain selection procedures using bootstrap samples, which have not been used before, will be investigated, and the partial Gauss discrepancy will be extended to the case of logistic regression. Recommendations will be made as to which approaches are the most suitable or most practical in different situations. Most statistical texts deal with issues regarding prediction, whereas the epidemiological literature focuses on estimation. It is therefore hoped that the thesis will be a useful reference for those, statistically or epidemiologically trained, who have to deal with issues regarding variable selection in logistic regression. When fitting models in general, and logistic regression models in particular, it is standard practice to determine the goodness of fit of models, and to ascertain whether outliers or influential observations are present in a data set. These aspects will not be discussed in this thesis, although they were considered when fitting the models.

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Books on the topic "Medical statistics"

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HK, Ramakrishna. Medical Statistics. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-1923-4.

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Mould, Richard F. Introductory medical statistics. 2nd ed. Bristol: Institue of Physics Pub., 1989.

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C, Sterne Jonathan A., and Kirkwood Betty R, eds. Essential medical statistics. 2nd ed. Malden, Mass: Blackwell Science, 2003.

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Mould, R. F. Introductory medical statistics. 2nd ed. Bristol: Adam Hilger, 1989.

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Ying, Lu, and Fang Jiqian, eds. Advanced medical statistics. New Jersey: World Scientific, 2003.

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Indrayan, Abhaya. Medical biostatistics. New York: Marcel Dekker, 2000.

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Indrayan, Abhaya. Medical biostatistics. 2nd ed. Boca Raton: Chapman & Hall/CRC, 2008.

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1970-, Taylor Gordon, ed. Medical statistics made easy 2. 2nd ed. Bloxham: Scion, 2008.

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HARRIS, M. Medical Statistics Made Easy. London: Taylor & Francis Group Plc, 2004.

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Gehan, Edmund A., and Noreen A. Lemak. Statistics in Medical Research. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2518-9.

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Book chapters on the topic "Medical statistics"

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Grieve, Andrew P. "Medical Statistics." In The Textbook of Pharmaceutical Medicine, 189–218. Oxford, UK: Blackwell Publishing Ltd., 2013. http://dx.doi.org/10.1002/9781118532331.ch9.

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Farewell, Vern T., and Daniel M. Farewell. "Medical Statistics." In International Encyclopedia of Statistical Science, 809–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_362.

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McParland, Brian J. "Collision Statistics." In Medical Radiation Dosimetry, 499–510. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5403-7_16.

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Everitt, Brian, and Sophia Rabe-Hesketh. "Prologue: Medical Statistics." In Analyzing Medical Data Using S-PLUS, 1–4. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3285-6_1.

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HK, Ramakrishna. "Introduction." In Medical Statistics, 1–2. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1923-4_1.

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HK, Ramakrishna. "Model Example." In Medical Statistics, 167–77. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1923-4_10.

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HK, Ramakrishna. "My Journey from a Rural Surgeon to an Author." In Medical Statistics, 3–8. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1923-4_2.

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HK, Ramakrishna. "Understanding Biostatistics, Probability, and Tests of Significance." In Medical Statistics, 9–19. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1923-4_3.

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HK, Ramakrishna. "Understanding Basic Statistical Terms." In Medical Statistics, 21–34. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1923-4_4.

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HK, Ramakrishna. "Tests of Significance." In Medical Statistics, 35–66. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1923-4_5.

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Conference papers on the topic "Medical statistics"

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Rajbhandary, Paurakh L., and Norbert J. Pelc. "Statistical bias in material decomposition in low photon statistics region." In SPIE Medical Imaging, edited by Christoph Hoeschen, Despina Kontos, and Thomas G. Flohr. SPIE, 2015. http://dx.doi.org/10.1117/12.2081326.

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Lei, Tianhu. "Statistics of MR signals: revisited." In Medical Imaging, edited by Jiang Hsieh and Michael J. Flynn. SPIE, 2007. http://dx.doi.org/10.1117/12.709388.

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Wu, Kenong, Steven Schreiner, Brent Mittelstadt, and Leland Witherspoon. "Image segmentation by gradient statistics." In Medical Imaging '98, edited by Kenneth M. Hanson. SPIE, 1998. http://dx.doi.org/10.1117/12.310885.

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Whiting, Bruce R., and Edward Muka. "Image quantization: statistics and modeling." In Medical Imaging '98, edited by James T. Dobbins III and John M. Boone. SPIE, 1998. http://dx.doi.org/10.1117/12.317025.

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Lei, Tianhu, and Jayaram K. Udupa. "Statistical properties of x-ray CT and MRI: from imaging physics to image statistics." In Medical Imaging 2002, edited by Larry E. Antonuk and Martin J. Yaffe. SPIE, 2002. http://dx.doi.org/10.1117/12.465626.

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Whiting, Bruce R. "Fundamental statistics of the imaging process." In Medical Imaging 1995, edited by Richard L. Van Metter and Jacob Beutel. SPIE, 1995. http://dx.doi.org/10.1117/12.208373.

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Stangl, Dalene. "Design of an internet course for training medical researchers in Bayesian statistical methods." In Training Researchers in the Use if Statistics. International Association for Statistical Education, 2000. http://dx.doi.org/10.52041/srap.00204.

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Access to statistical information is at an all-time high, and the information age is fuelling this access at an extraordinary pace. This access increases the capacity for medical researchers to use statistics to guide decision making, yet few courses teach methods to do so. Rarely does statistics training include methods for incorporating statistical output into decision making. Mass education and educational reform is needed. Technological advances of the past decade make this goal possible, and allow us to dramatically change how we use, teach, and think about statistics. This paper covers the conceptual development of an Internet continuing-education course designed to teach the basics the Bayesian statistics to medical researchers. Two questions are discussed: Why the Internet, and why the Bayesian paradigm?

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Brown, David G., and Robert F. Wagner. "Physics And Statistics Of Medical Imaging." In OE/LASE '89, edited by John C. Urbach. SPIE, 1989. http://dx.doi.org/10.1117/12.952852.

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Abbey, Craig K., Jascha N. Sohl-Dickstein, Bruno A. Olshausen, Miguel P. Eckstein, and John M. Boone. "Higher-order scene statistics of breast images." In SPIE Medical Imaging, edited by Berkman Sahiner and David J. Manning. SPIE, 2009. http://dx.doi.org/10.1117/12.813797.

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Lee, Sangyeol, Michael D. Abramoff, and Joseph M. Reinhardt. "Retinal atlas statistics from color fundus images." In SPIE Medical Imaging, edited by Benoit M. Dawant and David R. Haynor. SPIE, 2010. http://dx.doi.org/10.1117/12.843714.

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Reports on the topic "Medical statistics"

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Eremina, I. I., D. M. Lyisanov, and A. S. Pavlova. An automated application for collecting medical statistics. OFERNIO, October 2021. http://dx.doi.org/10.12731/ofernio.2021.24899.

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L, Santo, and Kang K. National Hospital Ambulatory Medical Care Survey: 2019 National Summary Tables. National Center for Health Statistics (U.S.), January 2023. http://dx.doi.org/10.15620/cdc:123251.

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The Ambulatory and Hospital Care Statistics Branch of the National Center for Health Statistics (NCHS) is pleased to release the most current nationally representative data on ambulatory care visits to physician offices in the United States. Statistics are presented on physician practices as well as patient and visit characteristics using data collected in the 2019 National Ambulatory Medical Care Survey (NAMCS). NAMCS is an annual nationally representative sample survey of visits to nonfederal office-based patient care physicians, excluding anesthesiologists, radiologists, and pathologists.

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Mian, Anam, and Holly Gross. ARL Academic Health Sciences Library Statistics 2022. Association of Research Libraries, November 2023. http://dx.doi.org/10.29242/hslstats.2022.

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This edition of the ARL Academic Health Sciences Library Statistics is a compilation of data that describes collections, expenditures, personnel, and services in medical libraries at ARL member institutions in the US and Canada in 2022.

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Santo, Loredana, Titilayo Okeyode,, and Susan Schappert. National Ambulatory Medical Care Survey–Community Health Centers: 2020 National Summary Tables. National Center for Health Statistics (U.S.), June 2022. http://dx.doi.org/10.15620/cdc:117687.

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The Ambulatory and Hospital Care Statistics Branch is pleased to release nationally representative estimates of ambulatory care visits made to both physicians and nonphysician clinicians (physician assistants [PAs], nurse practitioners [NPs], and nurse midwives) at community health centers (CHCs) in the United States. These web tables provide national estimates of visits to CHC providers and their characteristics.

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KHUDALOVA, M., V. FILONENKO, and E. KUDZOEVA. PSYCHOSOMATICS IN CONNECTION WITH THE AFFECTIVE DISORDERS OF PERSONALITY. Science and Innovation Center Publishing House, 2021. http://dx.doi.org/10.12731/2658-4034-2021-12-4-2-365-374.

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In recent years, we can notice a significant increase in psychosomatic disorders among borderline mental pathology, which are reasonably considered “the pathology of modern civilization”. The purpose of this study is to identify the relationship between psychosomatic disorders and the affective disorders of the personality. The study used the following methods: a diagnostic conversation and analysis of medical documents with the results of clinical examination, a scale for psychological express diagnostics of semi-structured depressive disorders (based on MMPI), a self-assessment scale by Ch.D. Spielberger - Yu.L. Hanin, Toronto Alexithymia Scale (TAS). Statistical methods of processing the empirical research results in the SPSS 22.0 program: descriptive statistics, correlation analysis (p-Spearman’s rank correlation). As a result of the study we can assert that psychosomatic disorders in respondents in the form of functional pathology of various organs and systems are connected with affective disorders in the form of moderate or severe depression of a neurotic level of various origins, alexithymia and high personal anxiety.

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Gindi, Renee. Health, United States, 2019. Centers for Disease Control and Prevention (U.S.), 2021. http://dx.doi.org/10.15620/cdc:100685.

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Health, United States, 2019 is the 43rd report on the health status of the nation and is submitted by the Secretary of the Department of Health and Human Services to the President and the Congress of the United States in compliance with Section 308 of the Public Health Service Act. This report was compiled by the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). The Health, United States series presents an annual overview of national trends in key health indicators. The 2019 report presents trends and current information on selected measures of morbidity, mortality, health care utilization and access, health risk factors, prevention, health insurance, and personal health care expenditures in a 20-figure chartbook. The Health, United States, 2019 Chartbook is supplemented by several other products including Trend Tables, an At-a-Glance table, and Appendixes available for download on the Health, United States website at: https://www.cdc.gov/nchs/hus/ index.htm. The Health, United States, 2019 Chartbook contains 20 figures and 20 tables on health and health care in the United States. Examining trends in health informs the development, implementation, and evaluation of health policies and programs. The first section (Figures 1–13) focuses on health status and determinants: life expectancy, infant mortality, selected causes of death, overdose deaths, suicide, maternal mortality, teen births, preterm births, use of tobacco products, asthma, hypertension, heart disease and cancer, and functional limitations. The second section (Figures 14–15) presents trends in health care utilization: use of mammography and colorectal tests and unmet medical needs. The third section (Figures 16–17) focuses on health care resources: availability of physicians and dentists. The fourth section (Figures 18–20) describes trends in personal health care expenditures, health insurance coverage, and supplemental insurance coverage among Medicare beneficiaries. The Highlights section summarizes major findings from the Chartbook. Suggested citation: National Center for Health Statistics. Health, United States, 2019. Hyattsville, MD. 2021.

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Brown, Yolanda, Twonia Goyer, and Maragaret Harvey. Heart Failure 30-Day Readmission Frequency, Rates, and HF Classification. University of Tennessee Health Science Center, December 2020. http://dx.doi.org/10.21007/con.dnp.2020.0002.

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30 Day Hospital Readmission Rates, Frequencies, and Heart Failure Classification for Patients with Heart Failure Background Congestive heart failure (CHF) is the leading cause of mortality, morbidity, and disability worldwide among patients. Both the incidence and the prevalence of heart failure are age dependent and are relatively common in individuals 40 years of age and older. CHF is one of the leading causes of inpatient hospitalization readmission in the United States, with readmission rates remaining above the 20% goal within 30 days. The Center for Medicare and Medicaid Services imposes a 3% reimbursement penalty for excessive readmissions including those who are readmitted within 30 days from prior hospitalization for heart failure. Hospitals risk losing millions of dollars due to poor performance. A reduction in CHF readmission rates not only improves healthcare system expenditures, but also patients’ mortality, morbidity, and quality of life. Purpose The purpose of this DNP project is to determine the 30-day hospital readmission rates, frequencies, and heart failure classification for patients with heart failure. Specific aims include comparing computed annual re-admission rates with national average, determine the number of multiple 30-day re-admissions, provide descriptive data for demographic variables, and correlate age and heart failure classification with the number of multiple re-admissions. Methods A retrospective chart review was used to collect hospital admission and study data. The setting occurred in an urban hospital in Memphis, TN. The study was reviewed by the UTHSC Internal Review Board and deemed exempt. The electronic medical records were queried from July 1, 2019 through December 31, 2019 for heart failure ICD-10 codes beginning with the prefix 150 and a report was generated. Data was cleaned such that each patient admitted had only one heart failure ICD-10 code. The total number of heart failure admissions was computed and compared to national average. Using age ranges 40-80, the number of patients re-admitted withing 30 days was computed and descriptive and inferential statistics were computed using Microsoft Excel and R. Results A total of 3524 patients were admitted for heart failure within the six-month time frame. Of those, 297 were re-admitted within 30 days for heart failure exacerbation (8.39%). An annual estimate was computed (16.86%), well below the national average (21%). Of those re-admitted within 30 days, 50 were re-admitted on multiple occasions sequentially, ranging from 2-8 re-admissions. The median age was 60 and 60% male. Due to the skewed distribution (most re-admitted twice), nonparametric statistics were used for correlation. While graphic display of charts suggested a trend for most multiple re-admissions due to diastolic dysfunction and least number due to systolic heart failure, there was no statistically significant correlation between age and number or multiple re-admissions (Spearman rank, p = 0.6208) or number of multiple re-admissions and heart failure classification (Kruskal Wallis, p =0.2553).

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Qin, Zhuzhu, Xutong Zheng, Xiaoling Zou, Danfeng Chen, Simin Huang, Bichun Huang, and Chenju Zhan. Status Quo of Stigma and Correlated Psychological Factors Among Breast Cancer Patients in China: A Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, April 2023. http://dx.doi.org/10.37766/inplasy2023.4.0012.

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Review question / Objective: This meta-analysis aimed to evaluate the level of stigma and the associated psychological factors among Chinese breast cancer patients. Condition being studied: According to the latest global cancer burden statistics provided by the International Agency for Research on Cancer (IARC) of the World Health Organization in 2020, breast cancer accounts for approximately 30% of the most common malignancies diagnosed in women worldwide.Breast cancer is a significant health concern for women in China. The estimated population diagnosed with breast cancer has been rising, with the estimated 2.5 million cases over the next decade. Despite the positive impact of advanced surgical treatment options, breast cancer patients often face additional challenges, such as breast deficiency, scarring, limb dysfunction, and altered body image. These physical changes can lead to psychological issues, such as a strong sense of shame and avoidance of reality, among breast cancer survivors. Therefore, it is important for medical professionals to consider not only the physical aspects of breast cancer treatment but also the psychological well-being of patients.

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Lucas, Christine, Emily Hadley, Jason Nance, Peter Baumgartner, Rita Thissen, David Plotner, Christine Carr, and Aerian Tatum. Machine Learning for Medical Coding in Health Care Surveys. National Center for Health Statistics (U.S.), October 2021. http://dx.doi.org/10.15620/cdc:109828.

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Prinja, Anil K., and Patrick O'Rourke. Transport in Random Media With Inhomogeneous Mixing Statistics. Office of Scientific and Technical Information (OSTI), May 2018. http://dx.doi.org/10.2172/1438709.

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