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Journal articles on the topic 'Uncertainty treatment'

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Author: Grafiati
Published: 11 March 2023

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1

McNAMARA, DAMIAN. "Physicians Face Osteoporosis-Treatment Uncertainty." Caring for the Ages 12, no. 7 (July 2011): 11. http://dx.doi.org/10.1016/s1526-4114(11)60187-x.

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Cheng, C. W., Y. Zheng, B. W. Wessels, J. A. Dorth, and D. B. Mansur. "Treatment Planning and Uncertainty Analysis." International Journal of Radiation Oncology*Biology*Physics 93, no. 3 (November 2015): E624—E625. http://dx.doi.org/10.1016/j.ijrobp.2015.07.2140.

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3

Zhang, Siliang, Ping Zhu, Wei Chen, and Paul Arendt. "Concurrent treatment of parametric uncertainty and metamodeling uncertainty in robust design." Structural and Multidisciplinary Optimization 47, no. 1 (May 24, 2012): 63–76. http://dx.doi.org/10.1007/s00158-012-0805-5.

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4

Cassidy, Rachel, and Charles F. Manski. "Tuberculosis diagnosis and treatment under uncertainty." Proceedings of the National Academy of Sciences 116, no. 46 (October 29, 2019): 22990–97. http://dx.doi.org/10.1073/pnas.1912091116.

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In 2017, 1.6 million people worldwide died from tuberculosis (TB). A new TB diagnostic test—Xpert MTB/RIF from Cepheid—was endorsed by the World Health Organization in 2010. Trials demonstrated that Xpert is faster and has greater sensitivity and specificity than smear microscopy—the most common sputum-based diagnostic test. However, subsequent trials found no impact of introducing Xpert on morbidity and mortality. We present a decision-theoretic model of how a clinician might decide whether to order Xpert or other tests for TB, and whether to treat a patient, with or without test results. Our first result characterizes the conditions under which it is optimal to perform empirical treatment; that is, treatment without diagnostic testing. We then examine the implications for decision making of partial knowledge of TB prevalence or test accuracy. This partial knowledge generates ambiguity, also known as deep uncertainty, about the best testing and treatment policy. In the presence of such ambiguity, we show the usefulness of diversification of testing and treatment.
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5

Norwood, Margaret. "Uncertainty of Treatment for Myalgic Encephalopathy." Physiotherapy 87, no. 12 (December 2001): 677–78. http://dx.doi.org/10.1016/s0031-9406(05)61121-2.

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6

Weise, K., and H. Zhang. "Uncertainty treatment in Monte Carlo simulation." Journal of Physics A: Mathematical and General 30, no. 17 (September 7, 1997): 5971–80. http://dx.doi.org/10.1088/0305-4470/30/17/008.

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7

Brown, James D. "Prospects for the open treatment of uncertainty in environmental research." Progress in Physical Geography: Earth and Environment 34, no. 1 (January 22, 2010): 75–100. http://dx.doi.org/10.1177/0309133309357000.

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Current treatments of uncertainty in environmental research embody several myths about the causes and consequences of imperfect knowledge, namely: (1) the dominant role of environmental factors in controlling uncertainty, such as system complexity, non-linearity and space-time variability, rather than social and psychological factors; (2) the primacy of observations in locating, quantifying, and reducing uncertainty; and (3) the value of technical assessments of uncertainty in ‘risk-based decision-making’. While the identification and treatment of specific sources of uncertainty remain impractical in some areas of environmental research, a source-based approach is increasingly used in environmental modeling. Here, selected sources of uncertainty are quantified with probability distributions and propagated to model outputs (a forward problem), while data are used to calibrate these estimates and reduce uncertainty (an inverse problem). More generally, current treatments of uncertainty and risk are dominated by attempts to quantify, minimize, and control uncertainty. Uncertainty is viewed as an ‘information deficit’ to be resolved, rather than an inherent product of conducting research. This paper argues for more open treatments of uncertainty in environmental research. Such openness requires an appreciation of the social and psychological causes of uncertainty, the role of observations as imperfect and contingent expressions of visible events, and the myriad ways in which scientific information can be misinterpreted, misused, or sidelined in environmental decision-making. The paper begins with a discussion of the nature and causes of uncertainty in environmental research. A review of current treatments of uncertainty is followed by an analysis of the source-based approach to assessing uncertainty. Prospects for the open treatment of uncertainty are then discussed in terms of circumventing the three ‘myths of uncertainty’ that characterize recent work in environmental research.
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Bender, Bernice K., and David M. Perkins. "Treatment of Parameter Uncertainty and Variability for a Single Seismic Hazard Map." Earthquake Spectra 9, no. 2 (May 1993): 165–95. http://dx.doi.org/10.1193/1.1585711.

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The inputs to probabilistic seismic hazard studies (seismic source zones, earthquake rates, attenuation functions, etc.) are uncertain, being based on subjective judgments and interpretations of limited data. In the face of this uncertainty, we consider (a) how one might “reasonably” determine the ground-motion levels to show on a single probabilistic seismic hazard map, and (b) the extent to which uncertainty in the calculated levels can be meaningfully represented on such a map. If the “best guess” estimates of the earthquake rate, the Gutenberg-Richter b-value and the maximum magnitude for a single source zone are regarded as uncorrelated and the uncertainty in each parameter can be regarded as symmetric about the estimated value, then the probabilistic ground-motion levels calculated using these best estimates represent both most likely values and also approximate mean values.
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9

Tötsch, Niklas, and Daniel Hoffmann. "Classifier uncertainty: evidence, potential impact, and probabilistic treatment." PeerJ Computer Science 7 (March 4, 2021): e398. http://dx.doi.org/10.7717/peerj-cs.398.

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Classifiers are often tested on relatively small data sets, which should lead to uncertain performance metrics. Nevertheless, these metrics are usually taken at face value. We present an approach to quantify the uncertainty of classification performance metrics, based on a probability model of the confusion matrix. Application of our approach to classifiers from the scientific literature and a classification competition shows that uncertainties can be surprisingly large and limit performance evaluation. In fact, some published classifiers may be misleading. The application of our approach is simple and requires only the confusion matrix. It is agnostic of the underlying classifier. Our method can also be used for the estimation of sample sizes that achieve a desired precision of a performance metric.
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Belia, E., Y. Amerlinck, L. Benedetti, B. Johnson, G. Sin, P. A. Vanrolleghem, K. V. Gernaey, et al. "Wastewater treatment modelling: dealing with uncertainties." Water Science and Technology 60, no. 8 (October 1, 2009): 1929–41. http://dx.doi.org/10.2166/wst.2009.225.

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This paper serves as a problem statement of the issues surrounding uncertainty in wastewater treatment modelling. The paper proposes a structure for identifying the sources of uncertainty introduced during each step of an engineering project concerned with model-based design or optimisation of a wastewater treatment system. It briefly references the methods currently used to evaluate prediction accuracy and uncertainty and discusses the relevance of uncertainty evaluations in model applications. The paper aims to raise awareness and initiate a comprehensive discussion among professionals on model prediction accuracy and uncertainty issues. It also aims to identify future research needs. Ultimately the goal of such a discussion would be to generate transparent and objective methods of explicitly evaluating the reliability of model results, before they are implemented in an engineering decision-making context.
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11

Nelzén, O. "Great uncertainty regarding treatment of varicose vein recurrence." Phlebologie 43, no. 01 (January 2014): 13–18. http://dx.doi.org/10.12687/phleb2172-1-2014.

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SummaryIntroduction: Although varicose vein recurrence is common and 10–30 % of all varicose vein surgery is done for recurrence of some sort, there are very few studies that can guide us to the best re-treatment option. With the introduction of minimal invasive endovenous treatments there is a variety of possible options besides traditional open surgical techniques.Method: The Scandinavian Venous Forum held a symposium at the GSP meeting in Lü-beck 2012 and this review article is based on data from the presentations at that symposium. Further data has been added regarding new knowledge that was not available a year ago, from PubMed search and article references.Results: The most common reasons for recurrence are discussed and also the discrepancy between neovascularization (NV) and recurrence due to technical failures. It is likely that NV is the most commonly duplex detected type of recurrence following open groin surgery, less common early after endovascular techniques. However, technical or tactical failures are the most common reasons for redo surgery because of symptomatic recurrence. NV seldom leads to symptomatic recurrences and thus a need for re- treatment. There is a risk that the stumps left following endovenous treatments will become a source for symptomatic recurrence after 5–10 years and indications of that have been reported in the few available 5 year RCT-reports following laser treatments. Treatment of recurrence due to stumps in the groin can be done safely within a reasonable operating time through a medial approach and the stump itself can generally not be treated with any of the endovenous alternative methods. Foam treatment can be used for most other recurrent veins but the durability is unknown. Endovascular thermal ablation can only be used for reopened or remaining saphenous veins and accessory saphenous veins while tributaries have to be treated by stab excisions or foam.Conclusion: Long term reports of results of redo surgery are limited but suggest reasonably good results from open surgical intervention and are non-existent for the endovenous techniques. So far groin recurrence seems best treated surgically by an indirect approach, preferably medial. More studies are needed to find the best treatment regime for varicose vein recurrence in general and hybrid procedures might be the way forward by combinations of different techniques.
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Tannehill, C. C., M. F. Dahab, W. E. Woldt, and I. Bogardi. "Evaluation of Nitrate Treatment Methods Under Uncertainty." Journal of Environmental Systems 25, no. 4 (January 1, 1996): 421–44. http://dx.doi.org/10.2190/gt8h-wh0e-jnp8-vf1n.

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WACHTER, KERRI. "IEED: Uncertainty Reigns In Diagnosis and Treatment." Clinical Psychiatry News 35, no. 11 (November 2007): 27. http://dx.doi.org/10.1016/s0270-6644(07)70720-8.

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Flage, R., and T. Aven. "On treatment of uncertainty in system planning." Reliability Engineering & System Safety 94, no. 4 (April 2009): 884–90. http://dx.doi.org/10.1016/j.ress.2008.09.011.

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15

Rouvray, Dennis H. "The treatment of uncertainty in the sciences." Endeavour 21, no. 4 (January 1997): 154–58. http://dx.doi.org/10.1016/s0160-9327(97)01076-4.

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16

Prescott, Hallie C., and Theodore J. Iwashyna. "Improving Sepsis Treatment by Embracing Diagnostic Uncertainty." Annals of the American Thoracic Society 16, no. 4 (April 2019): 426–29. http://dx.doi.org/10.1513/annalsats.201809-646ps.

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17

JOLMA, A., and J. NORTON. "Methods of uncertainty treatment in environmental models." Environmental Modelling & Software 20, no. 8 (August 2005): 979–80. http://dx.doi.org/10.1016/j.envsoft.2004.10.004.

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18

Grayson, Jonathan B. "OCD and Intolerance of Uncertainty: Treatment Issues." Journal of Cognitive Psychotherapy 24, no. 1 (February 2010): 3–15. http://dx.doi.org/10.1891/0889-8391.24.1.3.

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Recognition of the importance of intolerance of uncertainty (IU) as a critical cognitive distortion in anxiety disorders has been growing. The clinical challenge for researchers and clinicians is to understand the nature of IU and what it means to help clients to cope with it. Obsessive compulsive disorder (OCD) is the ideal diagnosis to examine the challenges IU can present, since, in the author’s opinion, it is the core of almost all manifestations of OCD. The article then focuses upon three factors in which IU factors play a role in successful/unsuccessful treatment: (a) the role of the therapist in helping the sufferer to understand IU and the goals of treatment; (b) techniques for helping the sufferer to accept and carry out the goals of treatment; and (c) patient factors/problems leading to “resistance” in coping with IU.
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19

Galambos, J. D., and J. A. Holmes. "Efficient Treatment of Uncertainty in Numerical Optimization." Risk Analysis 17, no. 1 (February 1997): 93–96. http://dx.doi.org/10.1111/j.1539-6924.1997.tb00847.x.

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20

Lombardo, Franklin T. "Treatment of Uncertainty for Windstorm Risk Assessment." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 5, no. 3 (September 2019): 02019001. http://dx.doi.org/10.1061/ajrua6.0001010.

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21

O'Donnell, Gregory E., and D. Brynn Hibbert. "Treatment of bias in estimating measurement uncertainty." Analyst 130, no. 5 (2005): 721. http://dx.doi.org/10.1039/b414843f.

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Reed, Dorothy A. "Treatment of uncertainty in structural damage assessment." Reliability Engineering & System Safety 39, no. 1 (January 1993): 55–64. http://dx.doi.org/10.1016/0951-8320(93)90147-q.

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23

BARONI, PIETRO, GIOVANNI GUIDA, and MASSIMILIANO GIACOMIN. "A- AND V-UNCERTAINTY: AN EXPLORATION ABOUT UNCERTAINTY MODELING FROM A KNOWLEDGE ENGINEERING PERSPECTIVE." International Journal on Artificial Intelligence Tools 16, no. 02 (April 2007): 161–94. http://dx.doi.org/10.1142/s0218213007003278.

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This paper presents a concrete experience of Knowledge Engineering, which, starting from a specific problem which occurred during the development of ASTRA, a knowledge-based system for preventive diagnosis of power transformers, turned out to provide significant insights concerning modeling of uncertain knowledge. In particular, it was observed that there are (at least) two conceptually distinct types of uncertainty affecting knowledge, namely uncertainty about applicability (A-uncertainty, for short) and uncertainty about validity (V-uncertainty, for short), which are different in nature and play different roles in uncertain reasoning. The concepts of A- and V-uncertainty are applicable in any context where uncertainty affecting domain knowledge can be ascribed to two kinds of sources: on the one hand, the existence of exceptions, on the other hand, deep-rooted doubts about the foundations themselves of the relevant domain knowledge. The introduction of these concepts allows one to define articulated uncertainty models, supporting the representation of the reasoning mechanisms used by experts in domains where both such uncertainty sources are present. This general claim was confirmed by the experience developed with ASTRA, where the explicit representation and management of A- and V-uncertainty enabled the correct treatment of some critical diagnostic cases.
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Wu, Yun-Chun, Ming-Chieh Shih, and Yu-Kang Tu. "Using Normalized Entropy to Measure Uncertainty of Rankings for Network Meta-analyses." Medical Decision Making 41, no. 6 (March 23, 2021): 706–13. http://dx.doi.org/10.1177/0272989x21999023.

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Ranking of treatments offers a straightforward interpretation of results derived from network meta-analysis. However, some published network meta-analyses have overemphasized treatment ranking without paying attention to its uncertainty. According to a review of 91 network meta-analyses, 52 reported treatment ranking, but 43 of them did not report the uncertainty of ranking. Without reporting the uncertainty, small differences in the ranking of treatments may be overinterpreted. Rankograms, cumulative rankograms, the credible/confidence interval of mean rank, the surface under the cumulative ranking curve (SUCRA), and the interquartile range of median rank have been used to show the uncertainty of rankings. However, it is not always straightforward to compare the differences in the distribution of probabilities by inspecting rankograms or to compare the intervals or ranges of treatment ranks. We therefore proposed normalized entropy, which transforms the distribution of ranking probabilities into a single quantitative measure, to facilitate a refined interpretation of uncertainty of treatment ranking. We used 4 real examples to demonstrate the uncertainty of ranking quantified by ranking probabilities, 95% confidence interval of SUCRA, and normalized entropy. We showed that as normalized entropy ranges from 0 to 1 and is independent of the number of treatments, it can be used to compare the uncertainty of treatment ranking within a network meta-analysis (NMA) and between different NMAs. Normalized entropy is an alternative tool for measuring the uncertainty of treatment ranking by improving the translation of results from NMAs to clinical practice and avoiding naïve interpretation of treatment ranking. We therefore recommend normalized entropy to be included in the presentation and interpretation of results from NMAs.
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25

Calvet, Xavier. "Dealing with uncertainty in the treatment of Helicobacter pylori." Therapeutic Advances in Chronic Disease 9, no. 4 (February 12, 2018): 93–102. http://dx.doi.org/10.1177/2040622318758240.

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Helicobacter pylori treatment may be viewed as an uncertain situation, where current knowledge is insufficient to provide evidence-based recommendations for all possible scenarios. Evidence suggests that, under uncertainty conditions, a few simple rules of thumb tend to work better than complex algorithms. Overall, five evidence-based rules of thumb are suggested: (1) Use four drugs; (2) Use maximal acid inhibition; (3) Treat for 2 weeks; (4) Do not repeat antibiotics after treatment failure; and (5) If your treatment works locally, keep using it. These simple rules of thumb may help the reader to select the best alternative for a given patient, choosing between the heterogeneous recommendations provided by the many different consensus conferences on H. pylori treatment recently published.
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Choubert, Jean-Marc, Samuel Martin Ruel, Cécile Miege, and Marina Coquery. "Rethinking micropollutant removal assessment methods for wastewater treatment plants – how to get more robust data?" Water Science and Technology 75, no. 12 (March 29, 2017): 2964–72. http://dx.doi.org/10.2166/wst.2017.181.

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This paper covers the pitfalls, recommendations and a new methodology for assessing micropollutant removal efficiencies in wastewater treatment plants. The proposed calculation rules take into account the limit of quantification and the analytical and sampling uncertainty of measured concentrations. We identified six cases for which a removal efficiency value is reliable and four other cases where result is highly variable (uncertain) due to very low or unquantified concentrations in effluent or when the influent–effluent concentrations differential is below the measurement uncertainty. The influence of the proposed calculation rules on removal efficiency values was scrutinized using actual results from a research project. The paper arrives at detailed recommendations for limiting the impact of other sources of uncertainty during sampling (sampling strategy, cleaning and field blank), chemical analyses (suspended solids and sludge) and data processing according to the targeted objectives.
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Saffiotti, Alessandro. "An AI view of the treatment of uncertainty." Knowledge Engineering Review 2, no. 2 (June 1987): 75–97. http://dx.doi.org/10.1017/s0269888900000795.

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AbstractThis paper reviews many of the very varied concepts of uncertainty used in AI. Because of their great popularity and generality “parallel certainty inference” techniques, so-called, are prominently in the foreground. We illustrate and comment in detail on three of these techniques; Bayes' theory (section 2); Dempster-Shafer theory (section 3); Cohen's model of endorsements (section 4), and give an account of the debate that has arisen around each of them. Techniques of a different kind (such as Zadeh's fuzzy-sets, fuzzy-logic theory, and the use of non-standard logics and methods that manage uncertainty without explicitly dealing with it) may be seen in the background (section 5).The discussion of technicalities is accompanied by a historical and philosophical excursion on the nature and the use of uncertainty (section 1), and by a brief discussion of the problem of choosing an adequate AI approach to the treatment of uncertainty (section 6). The aim of the paper is to highlight the complex nature of uncertainty and to argue for an open-minded attitude towards its representation and use. In this spirit the pros and cons of uncertainty treatment techniques are presented in order to reflect the various uncertainty types. A guide to the literature in the field, and an extensive bibliography are appended.
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Papiez, Kinga, Elizabeth Tutton, Emma E. Phelps, Janis Baird, Matthew L. Costa, Juul Achten, Phoebe Gibson, and Daniel C. Perry. "A qualitative study of parents’ and their child’s experience of a medial epicondyle fracture." Bone & Joint Open 2, no. 6 (June 1, 2021): 359–64. http://dx.doi.org/10.1302/2633-1462.26.bjo-2020-0186.r1.

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Aims The aim of this study was to explore parents and young people’s experience of having a medial epicondyle fracture, and their thoughts about the uncertainty regarding the optimal treatment. Methods Families were identified after being invited to participate in a randomized controlled trial of surgery or no surgery for displaced medial epicondyle fractures of the humerus in children. A purposeful sample of 25 parents (22 females) and five young people (three females, mean age 11 years (7 to 14)) from 15 UK hospitals were interviewed a mean of 39 days (14 to 78) from injury. Qualitative interviews were informed by phenomenology and themes identified to convey participants’ experience. Results The results identify parents’ desire to do the best for their child expressed through two themes: 1) ‘uncertainty’ (being uncertain about the best treatment option); and 2) ‘facilitating recovery’ (sharing the experience). Parents and young people were shocked when confronted with uncertainty about treatment and they felt responsible for the decision. They searched for further information, drew on their own experience, and struggled to weigh up risks of the treatments. Discussion with surgeons provided crucial support for decision-making, and young people were involved to a varying degree. In facilitating recovery, parents balanced increasing activity with protecting their child, but lacked knowledge about pain management, and how to improve strength and function of the arm. They hoped for a return to normal, including competitive sports. Conclusion Surgeons are aware of the impact of injury on children and their parents; however, they may be less aware of the turmoil created by treatment uncertainty. Confident surgeons who appreciate and contextualize the importance of pre-existing experience and beliefs are best placed to help the family develop confidence to embrace uncertainty, particularly regarding participation in clinical trials. Cite this article: Bone Jt Open 2021;2(6):359–364.
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Mackay, Eric, Justin Slater, Paul Arora, Kristian Thorlund, Audrey Beliveau, Devon J. Boyne, and Darren Brenner. "Error propagation in simulated treatment comparisons for multiple myeloma: Results from a simulation." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e20523-e20523. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e20523.

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e20523 Background: Comparing the effectiveness of multiple myeloma treatments presents a challenge due to the limited number of head-to-head trials with which to conduct indirect treatment comparisons. This is particularly true when subgroup analysis is of interest. In comparative effectiveness research Simulated Treatment Comparisons (STCs) are becoming increasingly common in the absence of head-to-head trials. STCs use estimates from limited IPD to adjust for covariate imbalance between trials, however the uncertainty from these estimates is generally ignored when estimating relative treatment effects. This study demonstrates the need to account for this uncertainty when conducting STCs for indications such as multiple myeloma. We introduce an STC method that accounts for the uncertainty due to covariate adjustment, and demonstrate its effectiveness via simulation. Methods: We simulated two single arm studies (N = 300 for both), each containing age and overall survival. We assume study 1 has individual patient data available, and study 2 only has aggregate age data and a digitized Kaplan-Meier curve. We compute a covariate adjustment term based on the mean age difference between the studies and the age coefficients from fitting a parametric survival model to the observed study 1 IPD. We then estimate the variance of this adjustment term via bootstrapping and incorporate this uncertainty into a Bayesian STC model which estimates the relative treatment effect for the two study datasets converted to a digitized Kaplan-Meier format. Results: The proportion of 95% credible intervals (CrI) that captured the true treatment effect was 86.8% without error propagation, whereas 92.0% of CrI’s captured the true treatment with error propagation. 94.9% of CrI’s contained the true treatment effect when using survival regression with the complete IPD. Conclusions: Failing to account for uncertainty from the covariate adjustment when conducting simulated treatment comparisons generally leads to underestimating the uncertainty of the relative treatment effect. This method better captures the uncertainty introduced when conducting an STC and has the potential to yield more reliable estimates of the comparative effectiveness of multiple myeloma treatments.
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Wray, Charlie M., and Lawrence K. Loo. "The Diagnosis, Prognosis, and Treatment of Medical Uncertainty." Journal of Graduate Medical Education 7, no. 4 (December 1, 2015): 523–27. http://dx.doi.org/10.4300/jgme-d-14-00638.1.

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Cortegiani, Andrea, Vincenzo Russotto, and Antonino Giarratano. "Lessons from uncertainty on antifungal treatment in ICU." Journal of Thoracic Disease 9, no. 4 (April 2017): E390—E391. http://dx.doi.org/10.21037/jtd.2017.03.65.

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Hoefer, A., G. Dirksen, J. Eyink, and E. M. Pauli. "Uncertainty Treatment for Level-2 Probabilistic Safety Analysis." Nuclear Science and Engineering 166, no. 3 (November 2010): 202–17. http://dx.doi.org/10.13182/nse10-09.

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von Furstenberg, George M. "Uncertainty in Macroeconomics: A Survey of its Treatment." Geneva Papers on Risk and Insurance - Issues and Practice 13, no. 1 (January 1988): 5–11. http://dx.doi.org/10.1057/gpp.1988.1.

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Lian, Jun, and Lei Xing. "Incorporating model parameter uncertainty into inverse treatment planning." Medical Physics 31, no. 9 (August 27, 2004): 2711–20. http://dx.doi.org/10.1118/1.1785451.

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Kujala, Heini, Mark A. Burgman, and Atte Moilanen. "Treatment of uncertainty in conservation under climate change." Conservation Letters 6, no. 2 (November 16, 2012): 73–85. http://dx.doi.org/10.1111/j.1755-263x.2012.00299.x.

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Jette, Diane U., and Alan M. Jette. "Professional uncertainty and treatment choices by physical therapists." Archives of Physical Medicine and Rehabilitation 78, no. 12 (December 1997): 1346–51. http://dx.doi.org/10.1016/s0003-9993(97)90308-7.

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Gerardy, I., J. Ródenas, and S. Gallardo. "Uncertainty analysis in MCNP5 calculations for brachytherapy treatment." Applied Radiation and Isotopes 69, no. 8 (August 2011): 1108–11. http://dx.doi.org/10.1016/j.apradiso.2011.02.003.

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Pfeffer, Marc A. "Anemia treatment in chronic kidney disease: shifting uncertainty." Heart Failure Reviews 13, no. 4 (April 10, 2008): 425–30. http://dx.doi.org/10.1007/s10741-008-9087-9.

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Cortegiani, Andrea, Vincenzo Russotto, Santi Maurizio Raineri, Cesare Gregoretti, and Antonino Giarratano. "Uncertainty about the evidence on untargeted antifungal treatment." European Journal of Internal Medicine 37 (January 2017): e18-e19. http://dx.doi.org/10.1016/j.ejim.2016.08.029.

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Urie, M. M., M. Goitein, K. Doppke, J. G. Kutcher, T. LoSasso, R. Mohan, J. E. Munzenrider, M. Sontag, and J. W. Wong. "The role of uncertainty analysis in treatment planning." International Journal of Radiation Oncology*Biology*Physics 21, no. 1 (May 1991): 91–107. http://dx.doi.org/10.1016/0360-3016(91)90170-9.

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Gujer, Willi. "Is modeling of biological wastewater treatment a mature technology?" Water Science and Technology 63, no. 8 (April 1, 2011): 1739–43. http://dx.doi.org/10.2166/wst.2011.323.

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Based on three case studies it is demonstrated that the application of mathematical models in biological wastewater treatment has not yet reached its full potential. Model structure uncertainty and correlation of uncertain parameter values make up the first case. The combination of biokinetic and hydraulic models relates to the second case. The evolution of a full scale plant over its life expectancy is the frame for the third case. This paper was initially presented as a discussion starter and thus is raising questions rather than providing answers.
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Retzbach, Joachim, Andrea Retzbach, Michaela Maier, Lukas Otto, and Marion Rahnke. "Effects of Repeated Exposure to Science TV Shows on Beliefs About Scientific Evidence and Interest in Science." Journal of Media Psychology 25, no. 1 (January 2013): 3–13. http://dx.doi.org/10.1027/1864-1105/a000073.

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For scientific laypersons, it is becoming increasingly important to understand the concept of scientific evidence. Prior research shows that the media tend to underrepresent the tentativeness and uncertainty of research findings. Yet presently it remains unclear whether beliefs about the certainty or uncertainty of scientific evidence are affected by the mass media’s science coverage. In this study, we tested cultivation effects of features from German science TV shows on laypersons’ beliefs about science and their interest in science. A sample of adults (N = 700) representative of the German population in terms of age, sex, and education participated in a longitudinal field experiment with an extended Solomon four-group design (two experimental and three control groups). The treatment consisted of six successive media stimuli, which portrayed scientific evidence as either certain or uncertain, over the course of 6 weeks. Results indicate media effects on participants’ beliefs about the certainty of scientific evidence. Additionally, the treatment portraying science as certain enhanced participants’ interest in science, whereas the uncertainty treatment had no effect on interest. These experimental findings demonstrate cultivation effects using real-world stimuli and are interpreted in support of the claim for a greater acknowledgement of uncertainty in science communication.
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Smeets, P. W. M. H., and G. J. Medema. "Combined use of microbiological and non-microbiological data to assess treatment efficacy." Water Science and Technology 54, no. 3 (August 1, 2006): 35–40. http://dx.doi.org/10.2166/wst.2006.445.

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The treatment efficacy for reducing Campylobacter concentrations by a drinking water treatment plant was assessed using a stochastic Monte Carlo model. The goal of the study was to reduce uncertainty of the results by combining microbiological and non-microbiological data in an advanced treatment assessment. Combining raw water Campylobacter and E. coli data reduced the uncertainty on raw water (peak) concentrations five-fold. Similar improvement was achieved for rapid sand filtration. Ozone disinfection was modelled based on ozone concentrations, contact time and temperature. Since this data was available, whereas most microbiological analyses at this point were negative, uncertainty was reduced three-fold. The slow sand filtration assessment could not be improved; however, since previous steps contained less uncertainty, this did not increase uncertainty by much. The study showed that using appropriate data for each treatment step can greatly reduce uncertainty in treatment assessment.
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Apóstolo, João Luís Alves, Catarina Sofia Castro Viveiros, Helena Isabel Ribeiro Nunes, and Helena Raquel Faustino Domingues. "Illness uncertainty and treatment motivation in type 2 diabetes patients." Revista Latino-Americana de Enfermagem 15, no. 4 (August 2007): 575–82. http://dx.doi.org/10.1590/s0104-11692007000400009.

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AIMS: To characterize the uncertainty in illness and the motivation for treatment and to evaluate the existing relation between these variables in individuals with type 2 diabetes. METHOD: Descriptive, correlational study, using a sample of 62 individuals in diabetes consultation sessions. The Uncertainty Stress Scale and the Treatment Self-Regulation Questionnaire were used. RESULTS: The individuals with type 2 diabetes present low levels of uncertainty in illness and a high motivation for treatment, with a stronger intrinsic than extrinsic motivation. A negative correlation was verified between the uncertainty in the face of the prognosis and treatment and the intrinsic motivation. DISCUSSION: These individuals are already adapted, acting according to the meanings they attribute to illness. Uncertainty can function as a threat, intervening negatively in the attribution of meaning to the events related to illness and in the process of adaptation and motivation to adhere to treatment. Intrinsic motivation seems to be essential to adhere to treatment.
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Fraas, Arthur G. "The Treatment of Uncertainty in EPA’s Analysis of Air Pollution Rules: A Status Report." Journal of Benefit-Cost Analysis 2, no. 2 (April 25, 2011): 1–27. http://dx.doi.org/10.2202/2152-2812.1022.

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An understanding of the uncertainty in benefit and cost estimates is a critical part of a benefit–cost analysis. Without a quantitative treatment of uncertainty, it is difficult to know how much confidence to place in the benefit–cost estimates associated with regulatory analysis. In 2002, an NRC report recommended that EPA move toward conducting probabilistic, multiple-source uncertainty analyses in its RIAs with the specification of probability distributions for major sources of uncertainty in the benefit estimates. In 2006, reports by GAO and RFF found that EPA had begun to address the NRC recommendations, but that much remained to be done to meet the NRC concerns. This paper provides a further review of EPA’s progress in developing a quantitative assessment of the uncertainties in its health benefits analyses for the RIAs for four recent rulemakings setting National Ambient Air Quality Standards (NAAQS). In conclusion, EPA’s basic approach to presenting the uncertainty in its health benefits estimates remains largely unchanged. Recent RIAs present the results of uncertainty analysis in piecemeal fashion rather than providing an overall, comprehensive statement of the uncertainty in the estimates. In addition, the uncertainty analysis in recent RIAs continues to focus on the concentration-response relationship and largely fails to address the uncertainty associated with the other key elements of the benefits analysis.
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Bednar, Filip. "Clinical Staging Uncertainty and Treatment Sequencing in Pancreatic Cancer." Annals of Surgery 275, no. 3 (November 18, 2021): 422–23. http://dx.doi.org/10.1097/sla.0000000000005317.

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Karamouz, M., E. Rasoulnia, Z. Zahmatkesh, M. A. Olyaei, and A. Baghvand. "Uncertainty-based flood resiliency evaluation of wastewater treatment plants." Journal of Hydroinformatics 18, no. 6 (August 12, 2016): 990–1006. http://dx.doi.org/10.2166/hydro.2016.084.

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Wastewater treatment plants (WWTPs) have a significant role in urban systems’ serviceability. These infrastructures, especially in coastal regions, are vulnerable to flooding. To minimize vulnerability, a better understanding of flood risk must be realized. To quantify the extent of efforts for flood risk management, a unified index is needed for evaluating resiliency as a key concept in understanding vulnerability. Here, a framework is developed to evaluate the resiliency of WWTPs in coastal areas of New York City. An analysis of the current understanding of vulnerability is performed and a new perspective utilizing different components including resourcefulness, robustness, rapidity, and redundancy is presented to quantify resiliency using a multi-criteria decision-making (MCDM) technique. To investigate the effect of certain factors of WWTPs on resiliency, uncertainty analysis is also incorporated in developing the framework. As a result, rather than a single value, a range of variation for each WWTP's resiliency is obtained. Finally, improvement of WWTPs’ performance is investigated by allocating financial resources. The results show the significant value of quantifying and improving resiliency that could be used in development of investment strategies. Consideration of uncertainty in the analysis is of great worth to estimate the potential room for improvement of resiliency of individual WWTPs.
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Bayraktarli, Yahya Y., Jack W. Baker, and Michael H. Faber. "Uncertainty treatment in earthquake modelling using Bayesian probabilistic networks." Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 5, no. 1 (March 2011): 44–58. http://dx.doi.org/10.1080/17499511003679931.

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O’Brien, Kevin. "Reducing uncertainty in orthodontic treatment: The wheel keeps turning." Journal of Orthodontics 46, no. 1_suppl (May 1, 2019): 26–28. http://dx.doi.org/10.1177/1465312519843911.

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Li, Lingling, Zhigang Li, Meng Wu, and Junjie Han. "Study on Treatment of Uncertainty Information in Reliability Measure." Advanced Science Letters 5, no. 2 (February 1, 2012): 976–80. http://dx.doi.org/10.1166/asl.2012.1840.

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