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Journal articles on the topic 'Medical uncertainty'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

Moores, Lisa K. "Medical Uncertainty." Chest 136, no. 4 (2009): 952–53. http://dx.doi.org/10.1378/chest.09-1092.

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2

Merz, Jon F. "Medical Uncertainty (Reprise)." Law, Medicine and Health Care 20, no. 3 (1992): 268–70. http://dx.doi.org/10.1111/j.1748-720x.1992.tb01202.x.

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3

Lynn, John T. "On medical uncertainty." American Journal of Medicine 96, no. 2 (1994): 186–87. http://dx.doi.org/10.1016/0002-9343(94)90140-6.

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4

Высоцкая, Елена Владимировна, Александр Иосифович Довнарь, and Анна Ивановна Печерская. "Medical decisions under uncertainty." Eastern-European Journal of Enterprise Technologies 3, no. 4(69) (2014): 16. http://dx.doi.org/10.15587/1729-4061.2014.23935.

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5

Sonnenberg, Amnon. "A medical uncertainty principle." American Journal of Gastroenterology 96, no. 12 (2001): 3247–50. http://dx.doi.org/10.1111/j.1572-0241.2001.05321.x.

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6

Sobal, Jeffery, and Bruce R. Deforge. "Medical Uncertainty in Students Entering Medical School." Sociological Focus 24, no. 4 (1991): 291–301. http://dx.doi.org/10.1080/00380237.1991.10570596.

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7

Zhukovskiy, V., and L. Smirnova. "UNCERTAINTY AND DISCRETE MAXIMIN." TAURIDA JOURNAL OF COMPUTER SCIENCE THEORY AND MATHEMATICS, no. 1 (November 25, 2022): 7–31. http://dx.doi.org/10.29039/1729-3901-2021-20-1-7-31.

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The article consists of two parts. The ﬁrst part is devoted to general questions that are related to uncertainty: causes and sources of uncertainties appearance, classiﬁcation of uncertainties in economic systems and approach to their assessment. In the second part the concept of maximin, based on the principle of guaranteed result (Wald’s principle) is considered. In this case, maximin is interpreted from viewpoint of two-level hierarchical game. On the basis of the maximin concept, a guaranteed solution in outcomes for K-stage positional single-criterion linear quadratic problem under uncert

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8

Beaird, Jack. "Medical Uncertainty and Practice Variation." Annals of Internal Medicine 132, no. 3 (2000): 246. http://dx.doi.org/10.7326/0003-4819-132-3-200002010-00021.

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9

Horne, L. Chad. "Medical Need, Equality, and Uncertainty." Bioethics 30, no. 8 (2016): 588–96. http://dx.doi.org/10.1111/bioe.12257.

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10

Scott, Ashley, Mark Sudlow, Emily Shaw, and James Fisher. "Medical education, simulation and uncertainty." Clinical Teacher 17, no. 5 (2020): 497–502. http://dx.doi.org/10.1111/tct.13119.

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11

Ranstam, J. "Sampling uncertainty in medical research." Osteoarthritis and Cartilage 17, no. 11 (2009): 1416–19. http://dx.doi.org/10.1016/j.joca.2009.04.007.

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12

Wellbery, Caroline. "The value of medical uncertainty?" Lancet 375, no. 9727 (2010): 1686–87. http://dx.doi.org/10.1016/s0140-6736(10)60725-8.

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13

Dittus, Robert S., Stephen D. Roberts, and James R. Wilson. "Quantifying uncertainty in medical decisions." Journal of the American College of Cardiology 14, no. 3 (1989): A23—A28. http://dx.doi.org/10.1016/0735-1097(89)90158-7.

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14

Ghosh, Amit K., and Shashank Joshi. "Tools to manage medical uncertainty." Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14, no. 5 (2020): 1529–33. http://dx.doi.org/10.1016/j.dsx.2020.07.055.

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15

Winkler, Robert L., and James E. Smith. "On Uncertainty in Medical Testing." Medical Decision Making 24, no. 6 (2004): 654–58. http://dx.doi.org/10.1177/0272989x04271045.

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16

Sassower, Raphael, and Michael A. Grodin. "Scientific uncertainty and medical responsibility." Theoretical Medicine 8, no. 2 (1987): 221–34. http://dx.doi.org/10.1007/bf00539757.

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17

Markowitz, Gerald, and David Rosner. "Expert panels and medical uncertainty." American Journal of Industrial Medicine 19, no. 1 (1991): 131–34. http://dx.doi.org/10.1002/ajim.4700190115.

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18

Aftab Khan, Kauser, Syed Mohmmad talha abbas Syed, Sumair Anwar, Zeeshan Sakhwat, and Iqra Chaudhry. "Major Factors Causing Uncertainties in Career Selection Among Medical Students of Gujranwala Medical College." Pakistan Journal of Public Health 12, no. 2 (2022): 55–59. http://dx.doi.org/10.32413/pjph.v12i2.945.

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Background: Diverse factors influence the career choice of medical students. This study was carried out to identify career preferences and factors influencing specialty selection among undergraduate medical students of Gujranwala Medical College which has students from multiple cities including Lahore, Rawalpindi, and Faisalabad to name a few. Hence this study will be valuable for the medical education sector because understanding career uncertainty at Gujranwala Medical College will also provide ways of developing better tools and counseling programs to help medical students throughout Pakist

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19

Simpkin, Arabella L., and Richard M. Schwartzstein. "Tolerating Uncertainty — The Next Medical Revolution?" New England Journal of Medicine 375, no. 18 (2016): 1713–15. http://dx.doi.org/10.1056/nejmp1606402.

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20

Lee, Young-Mee. "Transformation of medical education in uncertainty." Korean Journal of Medical Education 32, no. 2 (2020): 89–90. http://dx.doi.org/10.3946/kjme.2020.155.

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21

Szolovits, P. "Uncertainty and Decisions in Medical Informatics." Methods of Information in Medicine 34, no. 01/02 (1995): 111–21. http://dx.doi.org/10.1055/s-0038-1634594.

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Abstract:This paper presents a tutorial introduction to the handling of uncertainty and decision-making in medical reasoning systems. It focuses on the central role of uncertainty in all of medicine and identifies the major themes that arise in research papers. It then reviews simple Bayesian formulations of the problem and pursues the generalization to the Bayesian network methods that are popular today. Decision making is presented from the decision analysis viewpoint, with brief mention of recently-developed methods. The paper concludes with review of more abstract characterization of uncer

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22

Rughani, Amar. "Managing Medical Complexity: Uncertainty and Risk." InnovAiT: Education and inspiration for general practice 2, no. 11 (2009): 690–92. http://dx.doi.org/10.1093/innovait/inp154.

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23

BOREHAM, N. C. "MODELLING MEDICAL DECISION-MAKING UNDER UNCERTAINTY." British Journal of Educational Psychology 59, no. 2 (1989): 187–99. http://dx.doi.org/10.1111/j.2044-8279.1989.tb03090.x.

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24

Hartmann, W. H. "Preparing medical students to “manage uncertainty”." Academic Medicine 73, no. 2 (1998): 116–7. http://dx.doi.org/10.1097/00001888-199802000-00002.

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25

Johnson, Paul, and Alberta Hoi. "The art of managing medical uncertainty." Lancet 387, no. 10022 (2016): 1026. http://dx.doi.org/10.1016/s0140-6736(16)00207-5.

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26

Kerr, Anna M., and Charee M. Thompson. "Medical Students’ Reactions to Uncertainty During Clinical Rotations." Family Medicine 54, no. 4 (2022): 285–89. http://dx.doi.org/10.22454/fammed.2022.947719.

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Background and Objectives: Family physicians routinely manage uncertainty in their clinical practice. During their first year of clinical rotations, medical students learn communication and patient care skills that will influence the care they provide as future physicians. However, little is known about how their reactions to uncertainty change during this formative year, and medical education often fails to teach students how to manage uncertainty effectively. This study employs a repeated measures analysis of students’ reactions to uncertainty over the course of their third year. Methods: We

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27

Sicotte, Claude, Raynald Pineault, and Jean Lambert. "A Task Contingent Model of Medical Practice Organization." Health Services Management Research 6, no. 2 (1993): 117–28. http://dx.doi.org/10.1177/095148489300600206.

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The treatment of acute episodes of morbidities in hospitalized patients is a complex and uncertain task. To accomplish this task, physicians organize themselves into teams in an effort to overcome two constraints: The necessity to offer both complete care and continuous care. This study examines the extent to which the nature and size of medical teams is explained by task difficulty and task variability, which are two dimensions of task uncertainty. The results support globally the hypothesis that the organization of medical practice is contingent upon the nature of the task defined in terms o

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28

Kim, Min-sook. "Medical Service Purchasing Decision under Information Uncertainty." International Journal of IT-based Social Welfare Promotion and Management 3, no. 1 (2016): 227–32. http://dx.doi.org/10.21742/ijswpm.2016.3.36.

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29

Levine, Carol, and Ronald Bayer. "Screening Blood: Public Health and Medical Uncertainty." Hastings Center Report 15, no. 4 (1985): 8. http://dx.doi.org/10.2307/3561390.

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30

Beresford, Eric B. "Uncertainty and the Shaping of Medical Decisions." Hastings Center Report 21, no. 4 (1991): 6. http://dx.doi.org/10.2307/3562993.

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31

Han, Paul K. J. "Medical uncertainty: putting flesh on the bones." Patient Education and Counseling 104, no. 11 (2021): 2603–5. http://dx.doi.org/10.1016/j.pec.2021.09.001.

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32

Maurer, Brian T. "The principle of uncertainty in medical practice." Journal of the American Academy of Physician Assistants 25, no. 6 (2012): 61. http://dx.doi.org/10.1097/01720610-201206000-00014.

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33

White, Linda Johnson. "Clinical Uncertainty, Medical Futility and Practice Guidelines." Journal of the American Geriatrics Society 42, no. 8 (1994): 899–901. http://dx.doi.org/10.1111/j.1532-5415.1994.tb06568.x.

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34

LANE, JOHN, and SANDRO TSANG. "UNCERTAINTY IN THE ECONOMICS OF MEDICAL DECISIONS." Singapore Economic Review 53, no. 01 (2008): 81–101. http://dx.doi.org/10.1142/s0217590808002859.

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A physician chooses not only the supply of medical treatment, contingent on the result of a diagnostic test, but also the quality of his service. Two sources of uncertainty are introduced. One source arises as, based on the patient's "apparent" symptoms, only a priori estimates of the likelihood of alternative medical conditions can be inferred. They can be improved upon by a diagnostic test, but inherent in such tests is the possibility of a "false positive". This second source of uncertainty is shown to be critical in the possible over- or undersupply of medical treatment. Remedial pricing s

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35

Fortess, Eric E., and Marshall B. Kapp. "Medical Uncertainty, Diagnostic Testing, and Legal Liability." Law, Medicine and Health Care 13, no. 5 (1985): 213–18. http://dx.doi.org/10.1111/j.1748-720x.1985.tb00925.x.

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36

Meltzer, David. "Addressing uncertainty in medical cost–effectiveness analysis." Journal of Health Economics 20, no. 1 (2001): 109–29. http://dx.doi.org/10.1016/s0167-6296(00)00071-0.

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37

Reiner, Bruce. "Contextualizing Causation of Uncertainty in Medical Reporting." Journal of the American College of Radiology 15, no. 2 (2018): 325–27. http://dx.doi.org/10.1016/j.jacr.2017.08.019.

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38

Teo, Wendy Z. W., Lawrence H. Brenner, and B. Sonny Bal. "The Law and Social Values: Medical Uncertainty." Clinical Orthopaedics and Related Research® 475, no. 1 (2016): 27–30. http://dx.doi.org/10.1007/s11999-016-5127-2.

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39

Ristovski, Gordan, Tobias Preusser, Horst K. Hahn, and Lars Linsen. "Uncertainty in medical visualization: Towards a taxonomy." Computers & Graphics 39 (April 2014): 60–73. http://dx.doi.org/10.1016/j.cag.2013.10.015.

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40

Dardanoni, Valentino, and Adam Wagstaff. "Uncertainty and the demand for medical care." Journal of Health Economics 9, no. 1 (1990): 23–38. http://dx.doi.org/10.1016/0167-6296(90)90039-6.

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41

Emioma, D. U., O. F. Imaga, and S. O. Edeki. "The notion of fuzzy soft sets in medical ailment diagnosis." Journal of Physics: Conference Series 2199, no. 1 (2022): 012032. http://dx.doi.org/10.1088/1742-6596/2199/1/012032.

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Abstract People deal with the complexities of uncertain data; the most effective method for coping with uncertainty is the fuzzy set theory (Uncertain Sets) developed by Zadeh in 1965. This paper proposes a method to examine Sanchez’s medical diagnosis approach using Fuzzy Soft Complement in addition to a matrix representation of Fuzzy Soft Collection. Medical data from a particular hospital in Lagos, Nigeria, were collected and tested for diarrhea, cholera, and dysentery.

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42

Rajaraman, Sivaramakrishnan, Ghada Zamzmi, Feng Yang, Zhiyun Xue, Stefan Jaeger, and Sameer K. Antani. "Uncertainty Quantification in Segmenting Tuberculosis-Consistent Findings in Frontal Chest X-rays." Biomedicines 10, no. 6 (2022): 1323. http://dx.doi.org/10.3390/biomedicines10061323.

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Deep learning (DL) methods have demonstrated superior performance in medical image segmentation tasks. However, selecting a loss function that conforms to the data characteristics is critical for optimal performance. Further, the direct use of traditional DL models does not provide a measure of uncertainty in predictions. Even high-quality automated predictions for medical diagnostic applications demand uncertainty quantification to gain user trust. In this study, we aim to investigate the benefits of (i) selecting an appropriate loss function and (ii) quantifying uncertainty in predictions us

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43

Xu, Fang, Mengfan Yan, Lun Wang, and Shaojian Qu. "The Robust Emergency Medical Facilities Location-Allocation Models under Uncertain Environment: A Hybrid Approach." Sustainability 15, no. 1 (2022): 624. http://dx.doi.org/10.3390/su15010624.

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In emergency medical facilities location, the hierarchical diagnosis and treatment system plays an obvious role in the rational allocation of medical resources and improving the use efficiency of medical resources. However, few studies have investigated the operational mechanism of hierarchical medical systems in uncertain environments. To address this research gap, this paper proposes a hybrid approach for emergency medical facilities’ location-allocation. In the first stage, in order to concentrate on the utilization of medical resources, we choose alternative facility points from the whole

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44

Dunn, K., J. Zhang, C. E. Hsu, J. H. Holmes, and R. M. Maffei. "Understanding Behavioral Intent to Participate in Shared Decision-making in Medically Uncertain Situations." Methods of Information in Medicine 51, no. 04 (2012): 301–8. http://dx.doi.org/10.3414/me11-01-0077.

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SummaryObjective: This article describes the process undertaken to identify and validate behavioral and normative beliefs and behavioral intent based on the Theory of Reasoned Action (TRA) and applied to men between the ages of 45 and 70 in the context of their participation in shared decision-making (SDM) in medically uncertain situations. This article also discusses the preliminary results of the aforementioned processes and explores potential future uses of this information that may facilitate greater understanding, efficiency and effectiveness of clinician-patient consultations.Materials a

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45

Kurz, Alexander, Katja Hauser, Hendrik Alexander Mehrtens, et al. "Uncertainty Estimation in Medical Image Classification: Systematic Review." JMIR Medical Informatics 10, no. 8 (2022): e36427. http://dx.doi.org/10.2196/36427.

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Background Deep neural networks are showing impressive results in different medical image classification tasks. However, for real-world applications, there is a need to estimate the network’s uncertainty together with its prediction. Objective In this review, we investigate in what form uncertainty estimation has been applied to the task of medical image classification. We also investigate which metrics are used to describe the effectiveness of the applied uncertainty estimation Methods Google Scholar, PubMed, IEEE Xplore, and ScienceDirect were screened for peer-reviewed studies, published be

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46

Rashid, Fatima, Nida Hafiz, and Smruthy Chakka. "Clinical prioritisation questions to address medical uncertainty: A medical student perspective." Medical Teacher 42, no. 10 (2020): 1192–93. http://dx.doi.org/10.1080/0142159x.2020.1729966.

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47

He, Linwei, and Eline Smit. "Vague Language in Online Medical Consultation." European Journal of Health Communication 2, no. 1 (2021): 1–28. http://dx.doi.org/10.47368/ejhc.2021.001.

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Online medical consultation has become increasingly popular, while little is known about what features of such service can impact users’ emotions and behaviours. This study looked into the language features of online text-based medical consultation. Specifically, the aim of this paper was to examine the effects of vague language (i.e., non-specific, imprecise language) on health-related uncertainty, and its affective and behavioural consequences, while considering individual differences in regulatory focus. A between-subject (vague language vs. precise language vs. control condition) web-based

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48

Han, Paul K. J., Tania D. Strout, Caitlin Gutheil, et al. "How Physicians Manage Medical Uncertainty: A Qualitative Study and Conceptual Taxonomy." Medical Decision Making 41, no. 3 (2021): 275–91. http://dx.doi.org/10.1177/0272989x21992340.

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Background Medical uncertainty is a pervasive and important problem, but the strategies physicians use to manage it have not been systematically described. Objectives To explore the uncertainty management strategies employed by physicians practicing in acute-care hospital settings and to organize these strategies within a conceptual taxonomy that can guide further efforts to understand and improve physicians’ tolerance of medical uncertainty. Design Qualitative study using individual in-depth interviews. Participants Convenience sample of 22 physicians and trainees (11 attending physicians, 7

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49

Abeysinghe, Sudeepa, Claire Leppold, Akihiko Ozaki, and Mariko Morita. "Risk, uncertainty and medical practice: changes in the medical professions following disaster." Evidence & Policy: A Journal of Research, Debate and Practice 16, no. 2 (2020): 285–303. http://dx.doi.org/10.1332/174426419x15717232423159.

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Risk and uncertainty can destabilise and reconstruct the relationships between medicine, policy and publics. Through semi-structured interviews with medical staff following the Fukushima 3.11 Disaster, this paper demonstrates the way in which disruption (caused by disaster), coupled with uncertainty (in this case, around radiation risk) can serve to transform medical practices. After Fukushima, a deficit in publicly-trusted approaches to disaster management meant that the role and status of key medical professionals was transformed. This reorganisation of medical work included the development

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50

Oosterhuis, Wytze P., Hassan Bayat, David Armbruster, et al. "The use of error and uncertainty methods in the medical laboratory." Clinical Chemistry and Laboratory Medicine (CCLM) 56, no. 2 (2018): 209–19. http://dx.doi.org/10.1515/cclm-2017-0341.

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Abstract Error methods – compared with uncertainty methods – offer simpler, more intuitive and practical procedures for calculating measurement uncertainty and conducting quality assurance in laboratory medicine. However, uncertainty methods are preferred in other fields of science as reflected by the guide to the expression of uncertainty in measurement. When laboratory results are used for supporting medical diagnoses, the total uncertainty consists only partially of analytical variation. Biological variation, pre- and postanalytical variation all need to be included. Furthermore, all compon

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