Evolving Improved Sampling Protocols for Dose-Response Modelling Using Genetic Algorithms with a Profile-Likelihood Metric.

Practical limitations of quality and quantity of data can limit the precision of parameter identification in mathematical models. Model-based experimental design approaches have been developed to minimise parameter uncertainty, but the majority of these approaches have relied on first-order approximations of model sensitivity at a local point in parameter space. Practical identifiability approaches such as profile-likelihood have shown potential for quantifying parameter uncertainty beyond linear approximations. This research presents a genetic algorithm approach to optimise sample timing across various parameterisations of a demonstrative PK-PD model with the goal of aiding experimental design. The optimisation relies on a chosen metric of parameter uncertainty that is based on the profile-likelihood method. Additionally, the approach considers cases where multiple parameter scenarios may require simultaneous optimisation. The genetic algorithm approach was able to locate near-optimal sampling protocols for a wide range of sample number (n = 3-20), and it reduced the parameter variance metric by 33-37% on average. The profile-likelihood metric also correlated well with an existing Monte Carlo-based metric (with a worst-case r > 0.89), while reducing computational cost by an order of magnitude. The combination of the new profile-likelihood metric and the genetic algorithm demonstrate the feasibility of considering the nonlinear nature of models in optimal experimental design at a reasonable computational cost. The outputs of such a process could allow for experimenters to either improve parameter certainty given a fixed number of samples, or reduce sample quantity while retaining the same level of parameter certainty.

Large environmental changes reduce valence-dependent belief updating.

When updating beliefs, humans tend to integrate more desirable information than undesirable information. In stable environments (low uncertainty and high predictability), this asymmetry favors motivation towards action and perceived self-efficacy. However, in changing environments (high uncertainty and low predictability), this process can lead to risk underestimation and increase unwanted costs. Here, we examine how people (n = 388) integrate threatening information during an abrupt environmental change (mandatory quarantine during the COVID-19 pandemic). Given that anxiety levels are associated with the magnitude of the updating belief asymmetry; we explore its relationship during this particular context. We report a significant reduction in asymmetrical belief updating during a large environmental change as individuals integrated desirable and undesirable information to the same extent. Moreover, this result was supported by computational modeling of the belief update task. However, we found that the reduction in asymmetrical belief updating was not homogeneous among people with different levels of Trait-anxiety. Individuals with higher levels of Trait-anxiety maintained a valence-dependent updating, as it occurs in stable environments. On the other hand, updating behavior was not associated with acute anxiety (State-Anxiety), health concerns (Health-Anxiety), or having positive expectations (Trait-Optimism). These results suggest that highly uncertain environments can generate adaptive changes in information integration. At the same time, it reveals the vulnerabilities of individuals with higher levels of anxiety to adapt the way they learn.

Understanding the Impact of Communicating Uncertainty About COVID-19 in the News: Randomized Between-Subjects Factorial Experiment.

BACKGROUND: Whether and how the uncertainty about a public health crisis should be communicated to the general public have been important and yet unanswered questions arising over the past few years. As the most threatening contemporary public health crisis, the COVID-19 pandemic has renewed interest in these unresolved issues by both academic scholars and public health practitioners. OBJECTIVE: The aim of this study was to investigate the impact of communicating uncertainty about COVID-19-related threats and solutions on individuals' risk perceptions and misinformation vulnerability, as well as the sequential impact of these effects on health information processing and preventative behavioral intentions. METHODS: A 2×2 (threat uncertainty [presence vs absence]×solution uncertainty [presence vs absence]) full-fractional between-subjects online experiment was conducted with 371 Chinese adults. Focusing on the discussion of whether the asymptomatic cases detected during the COVID-19 pandemic would further lead to an uncontrolled pandemic, news articles were manipulated in terms of whether the infectiousness of asymptomatic cases and the means to control the transmission are presented in terms of their certainty or uncertainty. Participants were randomly assigned to one of the four experimental conditions, being instructed to read one news article. After reading the news article assigned, participants were asked to respond to a series of questions to assess their cognitive and behavioral responses. RESULTS: Individuals were more susceptible to believing false COVID-19-related information when a certain threat and uncertain solution were framed in the news article. Moreover, individuals' perceptions of crisis severity increased when exposed to news information containing uncertain solutions. Both misinformation vulnerability and perceived severity were positively associated with information processing. Information seeking was positively associated with protective behavioral intention, whereas information avoidance was negatively associated with protective behavioral intention. CONCLUSIONS: Our findings imply that uncertainty, depending on its aspect, can be effectively communicated to the public during an emerging public health crisis. These results have theoretical and practical implications for health communicators and journalists. Given its limited influence on individuals' cognitive and behavioral responses, uncertainty related to a health threat should be disseminated to meet the public's expectation of information transparency. However, caution is advised when communicating uncertainty related to potential solutions, as this factor exhibited a mixed impact on individual responses during a crisis.

Verbal Probability Terms for Communicating Clinical Risk - a Systematic Review.

Verbal probability expressions such as 'likely' and 'possible' are commonly used to communicate uncertainty in diagnosis, treatment effectiveness as well as the risk of adverse events. Probability terms that are interpreted consistently can be used to standardize risk communication. A systematic review was conducted. Research studies that evaluated numeric meanings of probability terms were reviewed. Terms with consistent numeric interpretation across studies were selected and were used to construct a Visual Risk Scale. Five probability terms showed reliable interpretation by laypersons and healthcare professionals in empirical studies. 'Very Likely' was interpreted as 90% chance (range 80 to 95%); 'Likely/Probable,' 70% (60 to 80%); 'Possible,' 40% (30 to 60%); 'Unlikely,' 20% (10 to 30%); and 'Very Unlikely' with 10% chance (5% to 15%). The corresponding frequency terms were: Very Frequently, Frequently, Often, Infrequently, and Rarely, respectively. Probability terms should be presented with their corresponding numeric ranges during discussions with patients. Numeric values should be presented as X-in-100 natural frequency statements, even for low values; and not as percentages, X-in-1000, X-in-Y, odds, fractions, 1-in-X, or as number needed to treat (NNT). A Visual Risk Scale was developed for use in clinical shared decision making.

Uncertainty Computation at Finite Distance in Nonlinear Mixed Effects Models-a New Method Based on Metropolis-Hastings Algorithm.

The standard errors (SE) of the maximum likelihood estimates (MLE) of the population parameter vector in nonlinear mixed effect models (NLMEM) are usually estimated using the inverse of the Fisher information matrix (FIM). However, at a finite distance, i.e. far from the asymptotic, the FIM can underestimate the SE of NLMEM parameters. Alternatively, the standard deviation of the posterior distribution, obtained in Stan via the Hamiltonian Monte Carlo algorithm, has been shown to be a proxy for the SE, since, under some regularity conditions on the prior, the limiting distributions of the MLE and of the maximum a posterior estimator in a Bayesian framework are equivalent. In this work, we develop a similar method using the Metropolis-Hastings (MH) algorithm in parallel to the stochastic approximation expectation maximisation (SAEM) algorithm, implemented in the saemix R package. We assess this method on different simulation scenarios and data from a real case study, comparing it to other SE computation methods. The simulation study shows that our method improves the results obtained with frequentist methods at finite distance. However, it performed poorly in a scenario with the high variability and correlations observed in the real case study, stressing the need for calibration.

Distinct neural markers of evidence accumulation index metacognitive processing before and after simple visual decisions.

Perceptual decision-making is affected by uncertainty arising from the reliability of incoming sensory evidence (perceptual uncertainty) and the categorization of that evidence relative to a choice boundary (categorical uncertainty). Here, we investigated how these factors impact the temporal dynamics of evidence processing during decision-making and subsequent metacognitive judgments. Participants performed a motion discrimination task while electroencephalography was recorded. We manipulated perceptual uncertainty by varying motion coherence, and categorical uncertainty by varying the angular offset of motion signals relative to a criterion. After each trial, participants rated their desire to change their mind. High uncertainty impaired perceptual and metacognitive judgments and reduced the amplitude of the centro-parietal positivity, a neural marker of evidence accumulation. Coherence and offset affected the centro-parietal positivity at different time points, suggesting that perceptual and categorical uncertainty affect decision-making in sequential stages. Moreover, the centro-parietal positivity predicted participants' metacognitive judgments: larger predecisional centro-parietal positivity amplitude was associated with less desire to change one's mind, whereas larger postdecisional centro-parietal positivity amplitude was associated with greater desire to change one's mind, but only following errors. These findings reveal a dissociation between predecisional and postdecisional evidence processing, suggesting that the CPP tracks potentially distinct cognitive processes before and after a decision.

Decision-making under conditions of explicit risk and uncertainty in autistic and typically developing adolescents and young adults.

Adolescence has been characterized as a period of risky and possibly suboptimal decision-making, yet the development of decision-making in autistic adolescents is not well understood. To investigate decision-making in autism, we evaluated performance on 2 computerized tasks capturing decision-making under explicit risk and uncertainty in autistic and non-autistic adolescents/young adults ages 12-22 years. Participants completed the Game of Dice Task (32 IQ-matched participant pairs) to assess decision-making under explicit risk and the modified Iowa Gambling Task (35 IQ-matched pairs) to assess decision-making under uncertainty. Autistic participants overall made riskier decisions than non-autistic participants on the Game of Dice Task, and the odds of making riskier decisions varied by age and IQ. In contrast, the autistic group showed comparable levels of learning over trial blocks to the non-autistic group on the modified Iowa Gambling Task. For both tasks, younger autistic participants performed poorer than their non-autistic counterparts, while group differences diminished in older ages. This age-related pattern suggests positive development during adolescence on risk assessment and decision-making in autism but also implies differential developmental trajectories between groups. These findings also suggest differential performance by the risk type, with additional complex influences of IQ and fluid cognition, which warrants further investigations.

Another nursing is possible: Ethics, political economies, and possibility in an uncertain world.

Overtaxed by the realities laid bare in the pandemic, nursing has imminent decisions to make. The exigencies of pandemic times overextend a health care infrastructure already groaning under the weight of inequitable distribution of resources and care commodified for profit. We can choose to prioritise different values. Invoking philosopher of science Isbelle Stengers's manifesto for slow science, this is not the only nursing that is possible. With this paper, I pick up threads of nursing's historical ontology, drawing previous scholarship on the historical narratives nurses use to understand themselves. Peeling back nursing's myth to alternate points of origin allows me to consider alternate lines of flight, a speculative adventure in paths not taken but paths that exist nonetheless. I go on to examine what a collective ethic of nursing could be, when we make space for these alternate histories, considering the confluences and conflicts that enable nurses to care and those that inhibit them from doing so. The imperative for this lies in the central importance of the reproductive labour of nursing health care, which leads me to a critique of nursing's capitulation to the pressures of late stage capitalism. This is a problem with ethical and ontological implications both for nursing, and also for those who require nursing care, an imperative to think about the kinds of present/futures for health, care, and health care we might cocreate in collaboration and solidarity with the communities in which nurses are imbricated, shedding the trappings of neoliberalism. There is significant power in the vision and praxis of 28 million nurses and midwives worldwide. Our ethics can guide our imagination which can in turn create possibility. This kind of endeavour-that of dreams and imagination-leads us to what could be, if only we leap.

Prediction of Individual Disease Progression Including Parameter Uncertainty in Rare Neurodegenerative Diseases: The Example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS).

The aim of this study was to develop a model to predict individual subject disease trajectories including parameter uncertainty and accounting for missing data in rare neurological diseases, showcased by the ultra-rare disease Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS). We modelled the change in SARA (Scale for Assessment and Rating of Ataxia) score versus Time Since Onset of symptoms using non-linear mixed effect models for a population of 173 patients with ARSACS included in the prospective real-world multicenter Autosomal Recessive Cerebellar Ataxia (ARCA) registry. We used the Multivariate Imputation Chained Equation (MICE) algorithm to impute missing covariates, and a covariate selection procedure with a pooled p-value to account for the multiply imputed data sets. We then investigated the impact of covariates and population parameter uncertainty on the prediction of the individual trajectories up to 5 years after their last visit. A four-parameter logistic function was selected. Men were estimated to have a 25% lower SARA score at disease onset and a moderately higher maximum SARA score, and time to progression (T50) was estimated to be 35% lower in patients with age of onset over 15 years. The population disease progression rate started slowly at 0.1 points per year peaking to a maximum of 0.8 points per year (at 36.8 years since onset of symptoms). The prediction intervals for SARA scores 5 years after the last visit were large (median 7.4 points, Q1-Q3: 6.4-8.5); their size was mostly driven by individual parameter uncertainty and individual disease progression rate at that time.

The mediating effect of uncertainty in illness between heart failure symptoms and health-related quality of life among rural patients with heart failure: A multi-center cross-sectional study.

BACKGROUND: The health-related quality of life (HRQoL) of patients with heart failure (HF) in rural settings in China remains unclear. Limited studies explored the mediating effect of uncertainty in illness between heart failure symptoms and HRQoL in this population. OBJECTIVES: To explore the status of HRQoL in rural patients with HF; assess the impact of HF symptoms and uncertainty in illness on HRQoL; and examine the mediating effect of uncertainty in illness on the relationship between symptoms and HRQoL in rural patients with HF. METHODS: Overall, 298 rural patients with HF were recruited from five township hospitals of Taishan and Jinzhong City in China between November 2021 and August 2022. Three variables, namely HF symptoms, uncertainty in illness, and HRQoL were measured using three validated scales. RESULTS: The average score of HRQoL in rural patients with HF was 43.19. Of the participants, 60.4 %, 35.23 %, and 4.37 % exhibited poor, moderate, and good HRQoL, respectively. The HF symptoms (ß = -0.47) and uncertainty in illness (ß = -0.34) directly influenced HRQoL. Moreover, the HF symptoms also indirectly affected HRQoL through uncertainty in illness (ß = -0.07). The indirect effect accounted for 12.96 % of the total effect of HF symptoms on HRQoL. CONCLUSION: Rural patients with HF exhibited poor HRQoL. In this population, HF symptoms and uncertainty in illness were negatively associated with HRQoL. Uncertainty in illness mediated the relationship between HF symptoms and HRQoL. Tailored healthcare services should be developed for the rural population to alleviate HF symptoms, reduce uncertainty in illness, and enhance their HRQoL.

The association between intolerance of uncertainty and depressive symptoms during COVID-19 in New York, USA.

BACKGROUND: Coronavirus Disease 2019 (COVID-19), a highly contagious respiratory illnesses, has globally impacted mental health. This study aims to investigate the association between intolerance of uncertainty and depressive symptoms during the pandemic in New York, USA, considering COVID-19-related worries as modifiers and mediators. METHOD: 1227 participants from three ongoing cohort studies, originally centered on trauma-exposed children and adolescents, provided data via questionnaires and telephone interviews across three waves. We used multivariable logistic and linear regression models to investigate the intolerance of uncertainty-depressive symptoms relationship, while adjusting for potential confounders and assessing the modification and mediation effects of Covid-19 related worries. RESULTS: Depressive symptoms prevalence was 18 %, 12 %, and 9 % at waves 0, 1, and 2 respectively. Strong positive associations were observed between intolerance of uncertainty above the median and depressive symptoms which remained significant after adjusting for potential confounders. Odds ratios were 2.14 (95 % CI: 1.54-2.99) and 4.50 (95 % CI: 2.67-7.93) for intolerance of uncertainty-depressive symptoms association at wave 0 and 1 respectively, and 3.22 (95 % CI: 1.68-6.63) for intolerance of uncertainty at wave 1 and depressive symptoms at wave 2. There was evidence of partial mediation by worries (12-37 %), but no evidence of a moderating effect. LIMITATION: It includes study's methodology, including self-report measures, remote data collection, and uncontrolled variables like anxiety and COVID-19 perspectives. CONCLUSION: The findings emphasize the importance of evidence-based strategies for tackling intolerance of uncertainty during pandemics, particularly in managing long COVID. Collaborative efforts between policymakers and clinicians are essential in this endeavor.

Partial label learning for automated classification of single-cell transcriptomic profiles.

Single-cell RNA sequencing (scRNASeq) data plays a major role in advancing our understanding of developmental biology. An important current question is how to classify transcriptomic profiles obtained from scRNASeq experiments into the various cell types and identify the lineage relationship for individual cells. Because of the fast accumulation of datasets and the high dimensionality of the data, it has become challenging to explore and annotate single-cell transcriptomic profiles by hand. To overcome this challenge, automated classification methods are needed. Classical approaches rely on supervised training datasets. However, due to the difficulty of obtaining data annotated at single-cell resolution, we propose instead to take advantage of partial annotations. The partial label learning framework assumes that we can obtain a set of candidate labels containing the correct one for each data point, a simpler setting than requiring a fully supervised training dataset. We study and extend when needed state-of-the-art multi-class classification methods, such as SVM, kNN, prototype-based, logistic regression and ensemble methods, to the partial label learning framework. Moreover, we study the effect of incorporating the structure of the label set into the methods. We focus particularly on the hierarchical structure of the labels, as commonly observed in developmental processes. We show, on simulated and real datasets, that these extensions enable to learn from partially labeled data, and perform predictions with high accuracy, particularly with a nonlinear prototype-based method. We demonstrate that the performances of our methods trained with partially annotated data reach the same performance as fully supervised data. Finally, we study the level of uncertainty present in the partially annotated data, and derive some prescriptive results on the effect of this uncertainty on the accuracy of the partial label learning methods. Overall our findings show how hierarchical and non-hierarchical partial label learning strategies can help solve the problem of automated classification of single-cell transcriptomic profiles, interestingly these methods rely on a much less stringent type of annotated datasets compared to fully supervised learning methods.

(e)Managing the uncertainty of tuberculosis in the post-Soviet limbo: Tracking prisoners' coerced mobility for treatment in Ukrainian prisons.

The collapse of the Soviet Union triggered an escalation of the tuberculosis (TB) epidemic in many post-Soviet countries, including Ukraine. The main reasons for this situation include both the approach to TB care and the concentration of TB cases in prisons. The neoliberal approach to TB care system reform promises the optimization of treatment terms, "dehospitalization" and "despecialization" of the system of care, and a different type of control, established through digital technologies. One such technology is the "e-TB Manager", which was designated as a national TB registry, including in the prison system in 2012. In prison, where everyone "is to be fixed" and isolated, the uncertainty of patients' movements seems to be avoided by pre-existing conditions. In practice, however, the vertically aligned, centralized organizational structure of the post-Soviet prison implies a constant need to link its elements together through "coerced" mobility carried out in secrecy. Treatment in exile may not be the primary goal of such a practice, but it becomes the result when prisoners from numerous prison facilities are sent to a limited number of prison TB hospitals. The integration of the e-TB Manager as a tool to enable the tracking of patient movements and, consequently, improve the efficiency of diagnostic and treatment processes in prison, can be seen as both a purely technical measure and a "magic bullet". In this article, we argue that, in the case of Ukrainian prisons, the neoliberal approach and the Soviet socialist approach to gaining control over TB indeed adapt and reinforce each other but fail to compete meaningfully. The fragmented implementation of one is absorbed by the fundamental and resilient nature of the other to produce and reproduce the state of "post-Soviet limbo". We use the "post-Soviet limbo" as an overall framework aimed at conceptualizing the post-Soviet transformation as a combination of efforts to avoid and manage the uncertainty of TB treatment, especially in prison. We examine the empirical case of coerced mobility of prisoners who require TB treatment, seeking to trace how this process is reflected in the e-TB Manager. We provide a more in-depth picture of this journey with details gathered from qualitative research materials to situate numbers and variables in their contexts, deconstructing the way the data are recorded according to the logic of the system in which they are produced.

Current uncertainties and challenges of publicly available pharmaceutical environmental risk assessment data.

Pharmaceutical residues are widely detected in aquatic environment worldwide mainly arising from human excretions in sewage systems. Presently, publicly available, high quality environmental risk assessment (ERA) data for pharmaceuticals are limited. However, databases like the Swedish Fass offer valuable resources aiding healthcare professionals and environmental scientists in identifying substances of significant concern. In this review, we provide a concise overview of the regulatory ERA process for medicinal products intended for human use. We explore its key assumptions and uncertainties using a subset of 37 pharmaceuticals. First, we compare the consistency of their predicted no-effect concentrations reported in the Fass database with those by marketing authorisation holders. Second, we compare the predicted environmental concentrations (PEC) calculated based on sales data between European and national drug consumption statistics as well as with measured environmental concentrations (MEC), to demonstrate their impact on the regional risk quotients. Finally, we briefly discuss the prevailing uncertainties and challenges of current ecotoxicity testing, especially outcomes of chronic and nonlethal effects.

Deep neural network uncertainty estimation for early oral cancer diagnosis.

BACKGROUND: Early diagnosis in oral cancer is essential to reduce both morbidity and mortality. This study explores the use of uncertainty estimation in deep learning for early oral cancer diagnosis. METHODS: We develop a Bayesian deep learning model termed 'Probabilistic HRNet', which utilizes the ensemble MC dropout method on HRNet. Additionally, two oral lesion datasets with distinct distributions are created. We conduct a retrospective study to assess the predictive performance and uncertainty of Probabilistic HRNet across these datasets. RESULTS: Probabilistic HRNet performs optimally on the In-domain test set, achieving an F1 score of 95.3% and an AUC of 96.9% by excluding the top 30% high-uncertainty samples. For evaluations on the Domain-shift test set, the results show an F1 score of 64.9% and an AUC of 80.3%. After excluding 30% of the high-uncertainty samples, these metrics improve to an F1 score of 74.4% and an AUC of 85.6%. CONCLUSION: Redirecting samples with high uncertainty to experts for subsequent diagnosis significantly decreases the rates of misdiagnosis, which highlights that uncertainty estimation is vital to ensure safe decision making for computer-aided early oral cancer diagnosis.

Bayes Optimal Integration of Social and Endogenous Uncertainty in Numerosity Estimation.

One of the most prominent social influences on human decision making is conformity, which is even more prominent when the perceptual information is ambiguous. The Bayes optimal solution to this problem entails weighting the relative reliability of cognitive information and perceptual signals in constructing the percept from self-sourced/endogenous and social sources, respectively. The current study investigated whether humans integrate the statistics (i.e., mean and variance) of endogenous perceptual and social information in a Bayes optimal way while estimating numerosities. Our results demonstrated adjustment of initial estimations toward group means only when group estimations were more reliable (or "certain"), compared to participants' endogenous metric uncertainty. Our results support Bayes optimal social conformity while also pointing to an implicit form of metacognition.

The mediating role of hope in the relation between uncertainty and social support with self-management among patients with ESKD undergoing hemodialysis.

BACKGROUND: Self-management behaviours are critical for patients requiring regular hemodialysis (HD) therapy. This study aimed to test the relationship between social support, uncertainty and self-management among HD patients and to explore whether hope plays a mediating role. METHODS: In a cross-sectional study, a convenience sample of 212 HD patients from two hospitals completed the Perceived Social Support Scale (PSSS), Herth Hope Index (HHI), Short form Mishel Uncertainty in Illness Scale (SF-MUIS), and hemodialysis Self-Management Instrument (HD-SMI). Data were analysed using structural equation modelling. RESULTS: The main finding indicated that social support positively affected self-management (ß = 0.50, t = 4.97, p < 0.001), and uncertainty negatively affected self-management (ß =-0.37, t=-4.12, p = < 0.001). In mediational model analysis, the effect of social support on self-management was fully mediated [(ß = 0.12; 95% BC CI (0.047, 0.228)] by hope. Also, the effect of uncertainty on self-management was fully mediated [(ß=- 0.014; 95% BC CI (-0.114, -0.003)] by hope. CONCLUSIONS: "Considering factors influencing self-management in HD patients is crucial for improving quality of life. Receiving support and informational resources can not only foster hope but also reduce their uncertainty, thus aiding in enhancing clinical outcomes, quality of life, and reducing complications. "Health care providers, especially nurses were advised to accept the existence of uncertainty, help patients make optimal use of support resources, and give more importance to disambiguation to reassure them. Therefore, well-designed interventions that enhance social support and hope and reduce uncertainty may help improve self-management behaviour in HD patients.

Measurement uncertainty of ethyl alcohol concentration in the exhaled breath of drivers and determination of sobriety at the time of incident. / Niepewnosc pomiaru stezenia alkoholu etylowego w powietrzu wydychanym kierowców oraz ustalenia stanu trzezwosci w chwili zdarzenia.

The aim of the study was to determine the components of measurement uncertainty in the concentration of alcohol in exhaled breath and to determine the state of sobriety at the time of incident. Based on the literature review and the authors' experience in providing opinions for law enforcement and the judiciary, the influence of various factors on the final interpretation of sobriety state is described on the basis of measurement uncertainty of breath analyzers, uncertainty of retrospective and prospective calculations, and uncertainty related to the conversion of alcohol concentrations detected during breath and blood tests. The paper pays particular attention to interpreting the concentrations of ethanol in exhaled breath close to the legal limits of the state of sobriety and the state after alcohol use, or the state after alcohol use and the state of insobriety. Analyzing the results of an exhaled breath test concerning concentrations close to the values of 0.1 mg/dm3 and 0.25 mg/dm3, it is necessary to take into account the factors affecting the measurements obtained, including the measurement uncertainty of the determination of alcohol in exhaled breath, the processes of absorption, distribution and metabolism of ethyl alcohol, and the possibility of the presence of alcohol lingering in the oral cavity. The incorrect execution of measurements of the tested person's alcohol concentration is also a problematic issue. When determining sobriety state by means of retrospective and prospective calculations, it is important to remember that the uncertainty of the result is affected by a number of factors and depends, among other things, on the information provided by the suspect. Hence, the expert should draw conclusions particularly cautiously and any overestimation or underestimation of the components of uncertainty can lead to erroneous conclusions. Awareness of the uncertainties inherent in the results of a sobriety test or alcohol calculation allows for meaningful interpretation of test results and determination of the sobriety state of the person tested.

Refining Mishel's Reconceptualized Uncertainty in Illness Theory: Explaining the Development of Uncertainty in Chronic Illness.

Background and Purpose: Mishel's Reconceptualized Uncertainty in Illness Theory describes the changed, more positive appraisal of uncertainty over time in a chronic disease. Therefore, Mishel referred to "probabilistic thinking" and "self-organization." The description of these concepts remained highly abstract, limiting the understanding of how change of uncertainty comes about. We aimed to elaborate on this gap and at refining the theory. Methods: We conducted a study consisting of three parts: (a) concept analyses of "probabilistic thinking" and "self-organization," (b) longitudinal qualitative study to investigate uncertainty experience over time, and (c) triangulation of (a) and (b) to develop theoretical propositions. Results: We developed five theoretical propositions in syllogistic form: (a) if persons experience uncertainty, they think probabilistically to assess the existentiality of potential consequences, (b) if they expect existential consequences, they experience uncertainty as a threat, (c) if the existentiality of uncertainty diminishes, then individuals accept uncertainty as an inherent part of illness, (d) if they accept uncertainty, they cognitively reframe it in a positive way in order to promote recovery, and (e) if persons reexperience uncertainty, they reassess the existentiality of potential consequences. Implications for Practice: We propose "health belief" as a mechanism driving "cognitive reframing" to explain the interrelation between uncertainty and a more positive experience. "Existential uncertainty" offers a new perspective on preventing a change in uncertainty experience. The new concepts can provide guidance to take measures to reduce existential uncertainty and promote health beliefs to change the experience of uncertainty from a negative to a more positive one.