Effects of response uncertainty and degree of knowledge on subjective uncertainty.
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Analysis of Variance↗
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Results for “Uncertainty”
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Subacute SNFs struggle to meet demands of Medicare PPS: uncertainty of ground rules, payment cuts pose biggest challenges. Part I: Managing uncertainty, reduced payments and patient admissions.
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Aged↗
A novel dose uncertainty model and its application for dose verification.
Based on statistical approach, a novel dose uncertainty model was introduced considering both nonspatial and spatial dose deviations. Non-space-oriented uncertainty is mainly caused by dosimetric uncertainties, and space-oriented dose uncertainty is the uncertainty caused by all spatial displacements. Assuming these two parts are independent, dose difference between measurement and calculation is a linear combination of nonspatial and spatial dose uncertainties. Two assumptions were made: (1) the relative standard deviation of nonspatial dose uncertainty is inversely proportional to the dose standard deviation sigma, and (2) the spatial dose uncertainty is proportional to the gradient of dose. The total dose uncertainty is a quadratic sum of the nonspatial and spatial uncertainties. The uncertainty model provides the tolerance dose bound for comparison between calculation and measurement. In the statistical uncertainty model based on a Gaussian distribution, a confidence level of 3sigma theoretically confines 99.74% of measurements within the bound. By setting the confidence limit, the tolerance bound for dose comparison can be made analogous to that of existing dose comparison methods (e.g., a composite distribution analysis, a gamma test, a chi evaluation, and a normalized agreement test method). However, the model considers the inherent dose uncertainty characteristics of the test points by taking into account the space-specific history of dose accumulation, while the previous methods apply a single tolerance criterion to the points, although dose uncertainty at each point is significantly different from others. Three types of one-dimensional test dose distributions (a single large field, a composite flat field made by two identical beams, and three-beam intensity-modulated fields) were made to verify the robustness of the model. For each test distribution, the dose bound predicted by the uncertainty model was compared with simulated measurements. The simulated measurements were within the tolerance bound as expected by a statistical prediction of the model. Using the dose uncertainty distributions, an uncertainty length (uncertainty area and uncertainty volume for two-dimensional and three-dimensional, respectively) histogram (a plot of the dose uncertainty of 1sigma received by a length of field) was made. The histogram provides additional information on superiority of a treatment plan in terms of uncertainty. In summary, the uncertainty model provides the dose comparison tool as well as the evaluation tool of a treatment planning system.
Algorithms↗
Uncertainty in prostate cancer. Ethnic and family patterns.
PURPOSE: Prostate cancer occurs 37% more often in African-American men than in white men. Patients and their family care providers (FCPs) may have different experiences of cancer and its treatment. This report addresses two questions: 1) What is the relationship of uncertainty to family coping, psychological adjustment to illness, and spiritual factors? and 2) Are these patterns of relationship similar for patients and their family care givers and for whites and African-Americans? DESCRIPTION OF STUDY: A sample of white and African-American men and their family care givers (N = 403) was drawn from an ongoing study, testing the efficacy of an uncertainty management intervention with men with stage B prostate cancer. Data were collected at study entry, either 1 week after post-surgical catheter removal or at the beginning of primary radiation treatment. Measures of uncertainty, adult role behavior, problem solving, social support, importance of God in one's life, family coping, psychological adjustment to illness, and perceptions of health and illness met standard criteria for internal consistency. Analyses of baseline data using Pearson's product moment correlations were conducted to examine the relationships of person, disease, and contextual factors to uncertainty. RESULTS: For family coping, uncertainty was significantly and positively related to two domains in white family care providers only. In African-American and white family care providers, the more uncertainty experienced, the less positive they felt about treatment. Uncertainty for all care givers was related inversely to positive feelings about the patient recovering from the illness. For all patients and for white family members, uncertainty was related inversely to the quality of the domestic environment. For everyone, uncertainty was related inversely to psychological distress. Higher levels of uncertainty were related to a poorer social environment for African-American patients and for white family members. For white patients and their family members, higher levels of uncertainty were related to lower scores on adult role behavior (shopping, running errands). For white family members, higher levels of uncertainty were related to less active problem solving and less perceived social support. Finally, higher levels of uncertainty were related to the importance of God for white patients and family care providers. CLINICAL IMPLICATIONS: The clearest finding of the present study is that there are ethnic differences in the relationship of uncertainty to a number of quality-of-life and coping variables. This has immediate implications for the assessment of psychosocial responses to cancer and cancer treatment. Much of what is in curricula is based on clinical and research experience primarily with white individuals. The experience of uncertainty related to cancer and its treatment is influenced by the cultural perspectives of patients and their families. To assist patients and families with the inevitable uncertainties of the cancer experience, healthcare providers need to reconsider their ethnocentric assumptions and develop more skill in assessing patient and family beliefs, values, cultural perspectives, and the influence of these on patient and family uncertainties.
Adaptation, Psychological↗
Determination of measurement uncertainty for the determination of triazines in groundwater from validation data.
Laboratories are increasingly urged to submit full uncertainties of their analytical results rather than only standard deviations. The determination of measurement uncertainties in compliance with the Guide to the Expression of Uncertainty in Measurement (GUM) is demonstrated using the validation approach explicitly endorsed by the recent edition of the EURACHEM guide for the determination of measurement uncertainty. Measurement uncertainty was split into uncertainty of the sample mass, uncertainty of the concentration of the stock standard solution, uncertainty of the calibration and uncertainty connected to within- and between-series precision. Uncertainties of sample mass and of the concentration of the stock standard solution were 0.26 and 1.14% for all analytes, which is negligible compared with the contributions of precision and calibration. Uncertainty of calibration was estimated from the calibration graph. Relative uncertainty of calibration was found to be strongly concentration dependent and to be the main uncertainty contribution below 0.2 microgram L-1. Precision was split into within-series and between-series standard deviation, which dominate the combined standard uncertainty at higher concentrations. The results obtained from these calculations are compared with results for a certified reference material and with the performance in an interlaboratory comparison. It was found that all results agreed within their uncertainty with the target values, showing that the estimated uncertainties are realistic.
Journal Article↗
Description of a generally applicable model for the evaluation of uncertainty of measurement in clinical chemistry.
There is a growing pressure on clinical chemistry laboratories to conform to quality standards that require the evaluation and expression of the uncertainty of results of measurement. Nevertheless, there is some reluctance to accept the uncertainty concept in the analytical community due to difficulty in evaluating uncertainty in practice. For example, often the uncertainty of some uncertainty components is not known very well in clinical chemistry measurements, such as those associated with matrix effects or with the values of the calibrators. Moreover, it is not clear how to interpret uncertainty in relation to diagnostic criteria, reference ranges and other decision limits in clinical chemistry practice. Hence, the value of reporting the uncertainty of the measurement result is not obvious. In this paper it is suggested a relatively simple, logical procedure for evaluating measurement uncertainty based on the principles in the Guide for the Expression of Uncertainty of Measurement (GUM). The measurement process is partitioned into elements that are well known to the analyst, namely sampling, calibration, and analysis. The corresponding model function expresses the result of a measurement as the value obtained by the analytical procedure multiplied by the correction factors for sampling bias, for bias caused by the calibrators, and for other types of bias. Under normal conditions, when the measurement procedure is validated and corrected for all known bias, the expected value of each correction factor is one. The uncertainty that remains with regard to sampling, manufacturing of calibrators and other types of bias is combined with the analytical imprecision to yield a combined uncertainty of a result of measurement. The advantages of this approach are: (i) Data from the method validation, internal quality control and from participation in external quality control schemes can be used as input in the uncertainty evaluation process. (ii) The partition of the measurement into well-defined tasks highlights the different responsibilities of the clinical chemistry laboratory and of the manufacturer of reagents and calibrators. (iii) The approach can be used to harmonize the uncertainty evaluation process, which is particularly relevant for laboratories seeking accreditation under ISO 17025. The application of the proposed model is demonstrated by evaluating the uncertainty of a result of a measurement of prolactin in human serum. In the example it is shown how to treat the uncertainty associated with a calibrator supplied with a commercial analytical kit, and how to evaluate the uncertainty associated with matrix effects.
Bias↗
Scenario-model-parameter: a new method of cumulative risk uncertainty analysis.
The recently developed concepts of aggregate risk and cumulative risk rectify two limitations associated with the classical risk assessment paradigm established in the early 1980s. Aggregate exposure denotes the amount of one pollutant available at the biological exchange boundaries from multiple routes of exposure. Cumulative risk assessment is defined as an assessment of risk from the accumulation of a common toxic effect from all routes of exposure to multiple chemicals sharing a common mechanism of toxicity. Thus, cumulative risk constitutes an improvement over the classical risk paradigm, which treats exposures from multiple routes as independent events associated with each specific route. Risk assessors formulate complex models and identify many realistic scenarios of exposure that enable them to estimate risks from exposures to multiple pollutants and multiple routes. The increase in complexity of the risk assessment process is likely to increase risk uncertainty. Despite evidence that scenario and model uncertainty contribute to the overall uncertainty of cumulative risk estimates, present uncertainty analysis of risk estimates accounts only for parameter uncertainty and excludes model and scenario uncertainties. This paper provides a synopsis of the risk assessment evolution and associated uncertainty analysis methods. This evolution leads to the concept of the scenario-model-parameter (SW) cumulative risk uncertainty analysis method. The SMP uncertainty analysis is a multiple step procedure that assesses uncertainty associated with the use of judiciously selected scenarios and models of exposure and risk. Ultimately, the SMP uncertainty analysis method compares risk uncertainty estimates determined using all three sources of uncertainty with conventional risk uncertainty estimates obtained using only the parameter source. An example of applying the SMP uncertainty analysis to cumulative risk estimates from exposures to two pesticides indicates that inclusion of scenario and model sources.
Environmental Pollutants↗
Uncertainty estimation on the quantification of major milk proteins by liquid chromatography.
A detailed uncertainty budget is evaluated for the quantification of major milk proteins separated by reversed phase ion-pair high performance liquid chromatography (RP-IP HPLC). Recommendations from the International Organisation for Standardisation, Guide to the Expression of Uncertainty and the EURACHEM/CITAC Guide "Quantifying Uncertainty in Analytical Measurement" were followed resulting in the expression of combined uncertainties as an expanded uncertainty. The authors have identified the lack of a detailed description on all possible sources of uncertainty on chromatographic milk protein separations. Up to now, uncertainty has only been expressed as a random chromatographic variability, being exclusively measured by regression equations. On the contrary, the establishment of a full uncertainty budget permits a complete identification of all sources of uncertainty associated with the measurement. Thus, a better comprehension of the method is obtained. Having identified all sources of uncertainty, to the best of our knowledge, they are minimised (whenever possible) and combined. The importance of this identification is crucial, in particular, concerning previously neglected sources, in this example the combination of the interpolated injected mass of protein and its purity, having a dominant influence on the total combined uncertainty. The quality of the measurement (measured by its associated uncertainty statement, which should be based on a full uncertainty budget evaluation) is demonstrated. Moreover, due to the high precision of the proposed method, the total protein content, in addition to the group protein composition (i.e. total casein and whey protein content) is calculated with high reliability, which is an extremely useful factor regarding the establishment of milk provenance/adulteration. Combined relative standard uncertainties ranging from 3.3 up to 12.5% for individual protein concentration were found, whereas for total protein content, an overall combined relative standard uncertainty of 2.7% (liquid) and 3.3% (powdered samples) was achieved.
Animals↗
Acidity constants in different media (I=0 and I=0.1 M KCl) from the uncertainty perspective.
Procedures for estimating the measurement uncertainty for the acidity constant Ka (or the pKa value) in different media (I=0 and I=0.1 mol L(-1) KCl), as determined by potentiometric titration, are presented. The uncertainty budgets (the relative contributions of the different input quantities to the uncertainty in the result) of the pKa (I=0) and pKa (I=0.1 mol L(-1) KCl) values are compared. Unlike the values themselves, the uncertainties and uncertainty budgets of the values are comparable. The uncertainty estimation procedures are based on mathematical models of pKa measurement and involve the identification and quantification of individual uncertainty sources according to the ISO GUM approach. The mathematical model involves 52 and 48 input parameters for pKa (I=0) and pKa (I=0.1 mol L(-1) KCl), respectively. The relative importance of each source of uncertainty is discussed. In both cases, the main contributors to the uncertainty budget are the uncertainty components due to the hydrogen ion concentration/activity measurement, which provide 63.7% (for pKa (I=0)) and 89.3% (for pKa (I=0.1 mol L(-1) KCl)) of the uncertainty. The remaining uncertainty contributions arise mostly from the limited purity of the acid. From this work, it is clear that the uncertainties of the pKa (I=0.1 mol L(-1) KCl) values tend to be lower than those of the pKa (I=0) values. The main reasons for this are that: (1) the uncertainty due to the residual liquid junction potential is nominally absent in the case of pKa (I=0.1 mol L(-1) KCl) due to the similarly high concentrations of background electrolyte in the calibration solutions and measured solution; (2) the electrode system is more stable in solutions containing the 0.1 mol L(-1) KCl background electrolyte and so the readings obtained in these solutions are more stable.
Journal Article↗
Evaluating uncertainty in environmental life-cycle assessment. A case study comparing two insulation options for a Dutch one-family dwelling.
The evaluation of uncertainty is relatively new in environmental life-cycle assessment (LCA). It provides useful information to assess the reliability of LCA-based decisions and to guide future research toward reducing uncertainty. Most uncertainty studies in LCA quantify only one type of uncertainty, i.e., uncertainty due to input data (parameter uncertainty). However, LCA outcomes can also be uncertain due to normative choices (scenario uncertainty) and the mathematical models involved (model uncertainty). The present paper outlines a new methodology that quantifies parameter, scenario, and model uncertainty simultaneously in environmental life-cycle assessment. The procedure is illustrated in a case study that compares two insulation options for a Dutch one-family dwelling. Parameter uncertainty was quantified by means of Monte Carlo simulation. Scenario and model uncertainty were quantified by resampling different decision scenarios and model formulations, respectively. Although scenario and model uncertainty were not quantified comprehensively, the results indicate that both types of uncertainty influence the case study outcomes. This stresses the importance of quantifying parameter, scenario, and model uncertainty simultaneously. The two insulation options studied were found to have significantly different impact scores for global warming, stratospheric ozone depletion, and eutrophication. The thickest insulation option has the lowest impact on global warming and eutrophication, and the highest impact on stratospheric ozone depletion.
Air Pollution, Indoor↗
PM2.5 speciation trends network: evaluation of whole-system uncertainties using data from sites with collocated samplers.
The objectives of this paper are to contrast the relative variability of replicate laboratory measurements of selected chemical components of fine particulate matter (PM) with total variability from collocated measurements and to compare the magnitudes of the uncertainties determined from collocated sampler data with those currently being provided to U.S. Environmental Protection Agency (EPA)'s Air Quality System (AQS) database by RTI International (RTI). Pointwise uncertainty values are needed for modeling and data analysis and should include all the random errors affecting each data point. Total uncertainty can be decomposed into two primary components: analytical measurement uncertainty and sampling uncertainty. Analytical measurement uncertainties are relatively easy to calculate from routine quality control (QC) data. Sampling uncertainties, on the other hand, are comparatively difficult to measure. In this paper, the authors describe data from collocated samplers to provide a snapshot of whole-system uncertainty for several important chemical species. The components of uncertainty were evaluated for key species from each of the analytical methods employed by the PM2.5 Speciation Trends Network (STN) program: gravimetry, ion chromatography (IC), X-ray fluorescence (XRF), and thermal-optical analysis for organic carbon and elemental carbon. The results show that the laboratory measurement uncertainties are typically very small compared with uncertainties calculated from the differences between samples collected from collocated samplers. These differences are attributable to the "field" components uncertainty, which may include contamination and/or losses during shipping, handling, and sampling, as well as other distortions of the concentration level due to flow and sample volume variations. Uncertainties calculated from the collocation results were found to be generally similar to the uncertainties currently being loaded into EPA's AQS system, with some exceptions described below.
Air Pollutants↗
An uncertainty budget for the measurement of ethanol in blood by headspace gas chromatography.
An uncertainty budget was constructed for the measurement of ethanol in blood by headspace gas chromatography. The uncertainty budget, covering the analytical range of ethanol concentrations up to 3.00 g/kg, included analytical uncertainty components, traceability uncertainty components, and effects caused by interindividual variation in blood water content. The analytical combined standard uncertainty was estimated from duplicate measurements of real samples and included contributions from headspace recovery, variation between columns, injection, repeatability of analytical signals, and statistical uncertainty of the calibration function. The traceability uncertainty was estimated in a sub-budget based on information about the calibrator and about the preparation of the aqueous standards. Two uncertainty components depended on the interindividual variation in blood water content. First, it caused uncertainty on the density of the blood, and second, it had an effect on the gas phase concentration of ethanol when doing the headspace sampling. These effects as well as their covariance were included in the uncertainty budget. For fresh blood samples, the analytical uncertainty was the dominating uncertainty component, accounting for approximately 90% of the variance. For blood samples collected 100 h postmortem, the interindividual variation in blood water content was the largest uncertainty component. It was demonstrated that subtracting a "safety margin" of 0.1 g/kg from the results was sufficient to keep the risk of committing a type 1 error below 0.1% in ethanol concentrations ranging up to 2 g/kg for fresh blood samples. This risk was higher for postmortem blood samples because of the higher uncertainty of measurement, but still less than approximately 1.4%.
Algorithms↗
Uncertainties in dose coefficients from ingestion of 131I, 137Cs, and 90Sr.
Quantification of uncertainties in doses from intakes of radionuclides is important in risk assessments and epidemiologic studies of individuals exposed to radiation. In this study, the uncertainties in the doses per unit intake (i.e., dose coefficients) for ingestion of 131I, 137Cs, and 90Sr by healthy individuals have been determined. Age-dependent thyroid dose coefficients were derived for 131I. The analysis for 131I uses recent measurements of thyroid volume obtained by ultrasonography, which indicate a thyroid mass lower than that previously obtained using autopsy measurements. The coefficients for 137Cs are determined using the relationship between the biological half-lives and the amount of potassium in the human body. The most recent International Commission on Radiological Protection biokinetic model was employed to determine the uncertainties for 90Sr. For 137Cs and 90Sr, the dose coefficients represent exposure in adulthood and they were determined for all organs of radiological importance. The uncertainty in the estimated dose coefficients represent state of knowledge estimates for a reference individual, and they are described by lognormal distributions with a specified geometric mean (GM) and geometric standard deviation (GSD). The estimated geometric means vary only slightly from the dose coefficients reported by ICRP publications. The largest uncertainty is observed in the dose coefficients for bone surface (GSD = 2.6), and red bone marrow (GSD = 2.4) in the case of ingestion of 90Sr. For most other organs, the uncertainty in the 90Sr dose coefficients is characterized by a GSD of 1.8 (or less for some organs). For 131I, the uncertainty in the thyroid dose coefficients is well represented by a GSD of 1.7 for both sexes and all ages other than infants for whom a GSD of 1.8 is more appropriate. The lowest uncertainties are obtained for the dose coefficients from ingestion of 137Cs (GSD = 1.24 for males; 1.4 for females). A dominant source of uncertainty in the ingestion dose coefficients is the variation of the biokinetic parameters. For 131I, the largest contribution to the uncertainty comes from the variation in the thyroid mass, but the contribution of the biokinetic parameters is comparable. The biokinetic parameters with the largest contribution to the uncertainty are (a) the fractional uptake from blood to thyroid in the case of ingestion of 131I, (b) the absorbed fraction from the gastrointestinal tract (f1) in the case of 90Sr, and (c) the amount of potassium in the body for 137Cs. The contribution to the uncertainty of the absorbed fraction (which accounts for the fraction of energy deposited in the target organ) is the smallest contributor to the uncertainty in the dose coefficients for most organs. To reduce the uncertainty in the dose estimated for a real individual, one should determine the above-mentioned parameters for the specified individual rather than to rely on assumptions for a reference individual.
Adolescent↗
Variants of uncertainty in decision-making and their neural correlates.
When leaving the tidy world of rules and people start judging probabilities on an intuitive basis, it revealed that they have some intuitions to choose from. One could refer to them as a family of subjective probability concepts or following Kahneman and Tversky, as variants of uncertainty. The authors distinguished between external and internal attributions of uncertainty and could show that the perceived reason of uncertainty determines the selected coping strategy. To investigate whether variants of uncertainty can also be distinguished on the cerebral level, two functional magnetic resonance imaging studies were conducted. Participants had to predict events (abstract visual stimuli) under parametrically varying degrees of (un-)certainty. In the first experiment, uncertainty was induced by the manipulation of event probability (externally attributed uncertainty). In the second experiment, uncertainty depended on participants' knowledge of valid rules of event occurrence, as trained before the experimental session (internally attributed uncertainty). As a result, parametric analyses revealed that activation within the posterior fronto-median cortex, particularly within mesial Brodmann area (BA) 8, increased with increasing uncertainty, no matter for which reason uncertainty emerged. Furthermore, it was found that different variants of uncertainty entailing different coping strategies can be dissociated due to additionally activated networks. Concluding, increasing activation within mesial BA 8 reflects that we are uncertain, additional networks what we do to resolve uncertainty in order to achieve future rewards. Hence, the phenomenological distinction between processes related to externally and internally attributed uncertainty is paralleled on the cerebral level.
Adaptation, Psychological↗
Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils.
Mapping the spatial distribution of soil pollutants is essential for delineating contaminated areas. Currently, geostatistical interpolation, kriging, is increasingly used to estimate pollutant concentrations in soils. The kriging-based approach, indicator kriging (IK), may be used to model the uncertainty of mapping. However, a smoothing effect is usually produced when using kriging in pollutant mapping. The detailed spatial patterns of pollutants could, therefore, be lost. The local uncertainty of mapping pollutants derived by the IK technique is referred to as the conditional cumulative distribution function (ccdf) for one specific location (i.e. single-location uncertainty). The local uncertainty information obtained by IK is not sufficient as the uncertainty of mapping at several locations simultaneously (i.e. multi-location uncertainty or spatial uncertainty) is required to assess the reliability of the delineation of contaminated areas. The simulation approach, sequential indicator simulation (SIS), which has the ability to model not only single, but also multi-location uncertainties, was used, in this study, to assess the uncertainty of the delineation of heavy metal contaminated soils. To illustrate this, a data set of Cu concentrations in soil from Taiwan was used. The results show that contour maps of Cu concentrations generated by the SIS realizations exhausted all the spatial patterns of Cu concentrations without the smoothing effect found when using the kriging method. Based on the SIS realizations, the local uncertainty of Cu concentrations at a specific location of x', refers to the probability of the Cu concentration z(x') being higher than the defined threshold level of contamination (z(c)). This can be written as Prob(SIS)[z(x')>z(c)], representing the probability of contamination. The probability map of Prob(SIS)[z(x')>z(c)] can then be used for delineating contaminated areas. In addition, the multi-location uncertainty of an area A,delineated as contaminated based on the probability map of Prob(SIS)[z(x')>z(c)], can be calculated to assess the reliability of delineation. Multi-location uncertainty refers to the probability of Cu concentrations in several locations, x'(1), x'(2), em leader, x'(m,) in the area A, being higher than the threshold (z(c)) as denoted by Prob(SIS)[z(x'(1))>z(c), z(x'(2))>z(c), em leader, andz(x'(m))>z(c)] or Prob(SIS)[z(A)>z(c)]. The multi-location uncertainty Prob(SIS)[z(A)>z(c)], obtained from the SIS, can be used to assess the reliability of delineation for regions suspected of contamination, (A), which has been delineated as contaminated. Reliance on this information facilitates the decision making process in determining which areas are contaminated and require cleanup action.
Copper↗
Percentiles of the product of uncertainty factors for establishing probabilistic reference doses.
Exposure guidelines for potentially toxic substances are often based on a reference dose (RfD) that is determined by dividing a no-observed-adverse-effect-level (NOAEL), lowest-observed-adverse-effect-level (LOAEL), or benchmark dose (BD) corresponding to a low level of risk, by a product of uncertainty factors. The uncertainty factors for animal to human extrapolation, variable sensitivities among humans, extrapolation from measured subchronic effects to unknown results for chronic exposures, and extrapolation from a LOAEL to a NOAEL can be thought of as random variables that vary from chemical to chemical. Selected databases are examined that provide distributions across chemicals of inter- and intraspecies effects, ratios of LOAELs to NOAELs, and differences in acute and chronic effects, to illustrate the determination of percentiles for uncertainty factors. The distributions of uncertainty factors tend to be approximately lognormally distributed. The logarithm of the product of independent uncertainty factors is approximately distributed as the sum of normally distributed variables, making it possible to estimate percentiles for the product. Hence, the size of the products of uncertainty factors can be selected to provide adequate safety for a large percentage (e.g., approximately 95%) of RfDs. For the databases used to describe the distributions of uncertainty factors, using values of 10 appear to be reasonable and conservative. For the databases examined the following simple "Rule of 3s" is suggested that exceeds the estimated 95th percentile of the product of uncertainty factors: If only a single uncertainty factor is required use 33, for any two uncertainty factors use 3 x 33 approximately 100, for any three uncertainty factors use a combined factor of 3 x 100 = 300, and if all four uncertainty factors are needed use a total factor of 3 x 300 = 900. If near the 99th percentile is desired use another factor of 3. An additional factor may be needed for inadequate data or a modifying factor for other uncertainties (e.g., different routes of exposure) not covered above.
Algorithms↗
Uncertainty in BRCA1 cancer susceptibility testing.
This study investigated uncertainty in individuals undergoing genetic counseling/testing for breast/ovarian cancer susceptibility. Sixty-three individuals from a single kindred with a known BRCA1 mutation rated uncertainty about 12 items on a five-point Likert scale before and 1 month after genetic counseling/testing. Factor analysis identified a five-item total uncertainty scale that was sensitive to changes before and after testing. The items in the scale were related to uncertainty about obtaining health care, positive changes after testing, and coping well with results. The majority of participants (76%) rated reducing uncertainty as an important reason for genetic testing. The importance of reducing uncertainty was stable across time and unrelated to anxiety or demographics. Yet, at baseline, total uncertainty was low and decreased after genetic counseling/testing (P = 0.004). Analysis of individual items showed that after genetic counseling/testing, there was less uncertainty about the participant detecting cancer early (P = 0.005) and coping well with their result (P < 0.001). Our findings support the importance to clients of genetic counseling/testing as a means of reducing uncertainty. Testing may help clients to reduce the uncertainty about items they can control, and it may be important to differentiate the sources of uncertainty that are more or less controllable. Genetic counselors can help clients by providing anticipatory guidance about the role of uncertainty in genetic testing.
Adolescent↗
Distinct neuropsychological processes may mediate decision-making under uncertainty with known and unknown probability in gain and loss frames.
Decision-making under uncertainty has been studied in psychiatry, economic psychology, and neuroeconomics. Psychiatric patients (e.g., drug addicts) often show low degrees of aversion to potential danger. Investigation into neuropsychological processing underlying decision under uncertainty is important for medical treatments of neuropsychiatric disorders. In economic decision theory, choices under uncertainty with and without known probabilities of outcomes have been discriminated. Regarding decision-making under uncertainty with known probabilities (risk), Kahnemann-Tversky's prospect theory revealed that people tend to avoid uncertainty in potential gains (i.e., risk-aversion in a gain-frame); on the contrary, prefer uncertainty in potential losses (i.e., risk-seeking in a loss-frame). Regarding choices between possible gains with known and unknown probabilities, Ellsberg demonstrated that, in a gain-frame, people tend to avoid uncertainty with unknown probabilities, more dramatically than uncertainty with known probabilities. This can be explained by risk/uncertainty aversion in the gain-frame, suggesting that uncertainty with known and unknown probabilities (i.e., risk and Knightian uncertainty, respectively) may possibly be processed by similar neuropsychological processing in the gain-frame. However, in spite of accumulating evidence in neuroimaging and neuroeconomic studies, it is still unknown whether probability and Knightian uncertainty in the loss-frame are mediated by similar neuropsychological processes as well. We propose that distinct neuropsychological processes may mediate potential losses with known and unknown probabilities, based on recent findings in neuroeconomics and our experiment. Importance of examining subjects' degree of Knightian uncertainty aversion for the prediction/treatment of drug addicts' risky behavior (e.g., needle-sharing) is also discussed.
Decision Making↗