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BACKGROUND: The dissolution test is a critical quality control method in the pharmaceutical industry, primarily used to assess drug bioavailability and ensure the consistency of manufactured batches. This test simulates the release of the active ingredient in the body and verifies compliance with specifications through multiple stages (e.g., S1, S2, S3). However, measurement uncertainty can undermine the reliability of test results, potentially leading to erroneous conformity decisions. This study aims to quantify the uncertainties arising from the dissolution, sampling, and quantification steps, as well as to estimate the risk of false conformity decisions in the dissolution test results for tablets. RESULTS: A comprehensive uncertainty evaluation was conducted for the dissolution, sampling, and quantification stages. The Monte Carlo method (MCM) was applied to assess the overall measurement uncertainty, which was determined to be approximately 5.2 %. The study revealed that sampling was the predominant contributor, accounting for 92 % of the total uncertainty, compared to 7 % from quantification and 1 % from dissolution steps. An MS Excel spreadsheet was developed to calculate the total risk value and classify it as either producer or consumer risk. This tool enables the evaluation of uncertainty in both individual tested units and mean values, depending on the stage criteria (e.g., S1, S2, S3). The proposed improved criteria were tested across various scenarios where the risk of false decisions due to measurement uncertainty was considered. These tests demonstrated the effectiveness of the criteria in managing consumer risk, highlighting the critical impact of sampling uncertainty on the decision-making process. SIGNIFICANCE AND NOVELTY: This study introduces novel, improved criteria for the dissolution test that account for the risk of false decisions due to measurement uncertainty. The proposed criteria significantly enhance the reliability of drug quality assessments. The study provides a robust framework for minimizing false conformity decisions. The development of a practical MS Excel tool further supports the reliable assessment of dissolution test results, ensuring higher standards of drug safety and efficacy in pharmaceutical quality control.

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OBJECTIVE: to analyze the meaning attributed by parents to the extended and permanent survival of childhood cancer. METHOD: qualitative narrative inquiry, developed with parents of adolescents and young adults who survived childhood cancer. Recruitment and data collection involved virtual and in-person approaches. The data were collected through semi-structured interviews. Data were analyzed according to reflective thematic analysis. RESULTS: a total of ten parents were included in the study. Two thematic narrative syntheses were constructed: "Times of war"; and "Time of uncertain peace", with their respective sub-themes. The cancer diagnosis marks the beginning of times of war in the parents' lives. They experience cancer treatment as "highs and lows" with potential threats to their children's lives. After that, "Time of uncertain peace" are reached, and the balance of the family unit is reestablished. However, the fear of recurrence makes the family peace uncertain, and its maintenance requires constant vigilance and attention to the signs and symptoms of a possible new battle. CONCLUSION: the results highlight the experience of being a parent of a childhood cancer survivor and can be applied to develop models of care centered on the survivors' family.

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This study aimed to analyze the role of nurses in hospital management in the face of COVID-19. The study had a qualitative, descriptive, and exploratory approach. The setting was a hospital that was completely transformed to care for patients with COVID-19. At the time of data collection, ten nurses managed the services, and all participated in the semi-structured interview. After thematic analysis, the data were presented in three categories, representing the elements of Donabedian's triad: structure, process, and result. Category 1 highlighted the hospital structure reconfiguration based on material and people management; category 2 addressed the work process restructuring to achieve goals with safety and quality; and category 3 focused on nurses' experiences in describing the results achieved and expected. The analysis highlighted the importance of teamwork, involvement, and adaptation of managers in the face of the challenges of a new and life-threatening disease, scarce resources, and the complexity of human relationships in the crisis. In transformational leadership, these nurses encouraged behavior change, professional growth, and resilience.

Objetivou-se analisar a atuação dos enfermeiros na gestão hospitalar frente à COVID-19. O estudo teve uma abordagem qualitativa, do tipo descritivo e exploratório. O cenário foi um hospital que se transformou totalmente para atendimento de pacientes com COVID-19. No momento da coleta de dados, dez enfermeiros estavam à frente da gestão dos serviços, e todos participaram da entrevista semiestruturada. Os dados, após análise temática, foram apresentados em três categorias representativas dos elementos da tríade de Donabedian, ou seja, estrutura, processo e resultado. A categoria 1 realçou a reconfiguração da estrutura hospitalar a partir da gestão de materiais e das pessoas; a categoria 2 abordou a reestruturação do processo de trabalho para alcance das metas com segurança e qualidade; e a categoria 3 focou nas experiências dos enfermeiros na descrição dos resultados alcançados e esperados. A análise evidenciou a importância do trabalho em equipe, do envolvimento e da adaptação do gestor diante dos desafios da doença nova e ameaçadora da vida, dos recursos escassos e da complexidade das relações humanas na crise. Na liderança transformacional esses enfermeiros incentivaram a mudança de comportamento, o crescimento profissional, e resiliência.

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Climate change is increasing the proportion of river networks experiencing flow intermittence, which in turn reduces local diversity (i.e., α-diversity) but enhances variation in species composition among sites (i.e., ß-diversity), with potential consequences on ecosystem stability. Indeed, the multiscale theory of stability proposes that regional stability can be attained not only by local processes but also by spatial asynchrony among sites. However, it is still unknown whether and how scale-dependent changes in biodiversity associated with river flow intermittence influence stability across spatial scales. To elucidate this, we here focus on multiple metacommunities of French rivers experiencing contrasting levels of flow intermittence. We clearly show that the relative contribution of spatial asynchrony to regional stability was higher for metacommunities of intermittent than perennial rivers. Surprisingly, spatial asynchrony was mainly linked to asynchronous population dynamics among sites, but not to ß-diversity. This finding was robust for both truly aquatic macroinvertebrates and for taxa that disperse aerially during their adult stages, implying the need to conserve multiple sites across the landscape to attain regional stability in intermittent rivers. By contrast, metacommunities of truly aquatic macroinvertebrates inhabiting perennial rivers were mainly stabilized by local processes. Our study provides novel evidence that metacommunities of perennial and intermittent rivers are stabilized by contrasting processes operating at different spatial scales. We demonstrate that flow intermittence enhances spatial asynchrony among sites, thus resulting in a regional stabilizing effect on intermittent river networks. Considering that climate change is increasing the proportion of intermittent rivers worldwide, our results suggest that managers need to focus on the spatial dynamics of metacommunities more than on local-scale processes to monitor, restore, and conserve freshwater biodiversity.

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In the event of oil spills in offshore oil and gas projects, containment and dispersion equipment must be sent to the affected areas within a critical time by vessels known as oil spill response vessels (OSRVs). Here, we developed an optimization tool, integrated with an oil spill trajectory simulation model, both in deterministic and stochastic alternatives, to support decision-making during the strategic planning of OSRV operations. The tool was constructed in Python using GNOME for oil spill simulations and the GUROBI to solve the optimization model. The tool was applied to a case study in Brazil and afforded relevant recommendations. In terms of research contributions, we proved the viability of the integration between oil spill simulation and mathematical modeling for OSRV strategic operation planning, we explored the stochasticity of the problem with an innovative strategy and we demonstrated flexibility and easy applicability of the framework on real operations.

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This study aims to address a critical gap in the literature by examining the incorporation of uncertainty in measuring carbon emissions using the greenhouse gas (GHG) Protocol methodology across all three scopes. By comprehensively considering the various dimensions of CO2 emissions within the context of organizational activities, our research contributes significantly to the existing body of knowledge. We address challenges such as data quality issues and a high prevalence of missing values by using information entropy, techniques for order preference by similarity to ideal solution (TOPSIS), and an artificial neural network (ANN) to analyze the contextual variables. Our findings, derived from the data sample of 56 companies across 18 sectors and 13 Brazilian states between 2017 and 2019, reveal that Scope 3 emissions exhibit the highest levels of information entropy. Additionally, we highlight the pivotal role of public policies in enhancing the availability of GHG emissions data, which, in turn, positively impacts policy-making practices. By demonstrating the potential for a virtuous cycle between improved information availability and enhanced policy outcomes, our research underscores the importance of addressing uncertainty in carbon emissions measurement for advancing effective climate change mitigation strategies.

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OBJECTIVE: To analyze the uncertainties experienced by nursing professionals who contracted COVID-19. METHODS: This qualitative research was conducted with 20 nursing professionals who fell ill from COVID-19. Data collection was carried out through semi-structured interviews; the data were then organized using thematic analysis and discussed in the context of Merle Mishel's Reconceptualized of Uncertainty in Illness Theory. RESULTS: The antecedents of the disease had a strong influence on how nursing professionals who contracted COVID-19 perceived uncertainty. The media coverage of the increasing number of cases, the collapse of the healthcare system, and the high mortality rate contributed to associating the disease with fear and panic. FINAL CONSIDERATIONS: Viewing it from the perspective of the disease's antecedents, the illness of a nursing professional from COVID-19 underscores that before being professionals, they are human beings just like anyone else, undergoing adversities and facing the possibilities associated with being ill.

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Some of the difficulties in numerical modeling of wireless communication devices for dosimetric evaluations arise from, e.g. incomplete documentation available for the numerical model, such as missing information on dielectric materials or the antenna matching circuitry. This study investigates the impact of these difficulties on the dosimetric results, such as the peak spatial average specific absorption rate at 900 and 1800 MHz and the peak spatial average power density at 28 GHz. The impact of dielectric losses, detuning, and mesh resolution is quantified using different generic and Computer Aided Design (CAD) based models of wireless transmitters. The findings show that the uncertainties of the numerical results due to detuning and mesh resolution can be reduced by normalization to the antenna feedpoint power instead of the feedpoint current. Uncertainties due to variations in dielectric losses can largely be compensated by normalization to the radiated power.

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BACKGROUND: The control of the microbial contamination of pharmaceutical products (PP) is crucial to ensure their safety and efficacy. The validity of the monitoring of such contamination depends on the uncertainty of this quantification. Highly uncertain quantifications due to the variability of determinations or the magnitude of systematic effects affecting microbial growth or other analytical operations make analysis unfit for the intended use. The quantification of the measurement uncertainty expressing the combined effects of all random and systematic effects affecting the analysis allows for a sound decision about quantification adequacy for their intended use. The complexity of the quantification of microbial analysis uncertainty led to the development of simplified ways of performing this evaluation. OBJECTIVE: This work assesses the adequacy of the simplified quantification of the uncertainty of the determination of the microbial contamination of PP by log transforming microbial count and dilution factor of the test sample whose uncertainty is combined in a log scale using the uncertainty propagation law. METHODS: This assessment is performed by a parallel novel bottom-up and accurate evaluation of microbial analysis uncertainty involving the Monte Carlo method simulation of the Poisson log-normal distribution of counts and of the normally distributed measured volumes involved in the analysis. Systematic effects are assessed and corrected on results to compensate for their impact on the determinations. Poisson regression is used to predict precision affecting determinations on unknown test samples. RESULT: Simplified and detailed models of the uncertainty of the measurement of the microbial contamination of PP are provided, allowing objective comparisons of several determinations and those with a maximum contamination level. CONCLUSIONS: This work concludes that triplicate determinations are required to produce results with adequately low uncertainty and that simplified uncertainty quantification underevaluates or overevaluates the uncertainty from determinations based on low or high colony numbers, respectively. Therefore, detailed uncertainty evaluations are advised for determinations between 50 and 200% of PP's maximum admissible contamination value. HIGHLIGHT: User-friendly tools for detailed and simplified evaluations of the uncertainty of the measurement of microbial contamination of PP are provided together with the understanding of when simplifications are adequate.

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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.

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Motivated by the implementation of a SARS-Cov-2 sewer surveillance system in Chile during the COVID-19 pandemic, we propose a set of mathematical and algorithmic tools that aim to identify the location of an outbreak under uncertainty in the network structure. Given an upper bound on the number of samples we can take on any given day, our framework allows us to detect an unknown infected node by adaptively sampling different network nodes on different days. Crucially, despite the uncertainty of the network, the method allows univocal detection of the infected node, albeit at an extra cost in time. This framework relies on a specific and well-chosen strategy that defines new nodes to test sequentially, with a heuristic that balances the granularity of the information obtained from the samples. We extensively tested our model in real and synthetic networks, showing that the uncertainty of the underlying graph only incurs a limited increase in the number of iterations, indicating that the methodology is applicable in practice.

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Climate change is one of the most important drivers of ecosystem change, the global-scale impacts of which will intensify over the next 2 decades. Estimating the timing of unprecedented changes is not only challenging but is of great importance for the development of ecosystem conservation guidelines. Time of emergence (ToE) (point at which climate change can be differentiated from a previous climate), a widely applied concept in climatology studies, provides a robust but unexplored approach for assessing the risk of ecosystem collapse, as described by the C criterion of the International Union for Conservation of Nature's Red List of Ecosystems (RLE). We identified 3 main theoretical considerations of ToE for RLE assessment (degree of stability, multifactorial instead of one-dimensional analyses, and hallmarks of ecosystem collapse) and 4 sources of uncertainty when applying ToE methodology (intermodel spread, historical reference period, consensus among variables, and consideration of different scenarios), which aims to avoid misuse and errors while promoting a proper application of the framework by scientists and practitioners. The incorporation of ToE for the RLE assessments adds important information for conservation priority setting that allows prediction of changes within and beyond the time frames proposed by the RLE.

Perspectivas sobre el momento del colapso ecosistémico en un clima cambiante Resumen El cambio climático es uno de los principales causantes del cambio ecosistémico, cuyo impacto a escala global se intensificará en las próximas dos décadas. No sólo es un reto estimar el momento de los cambios sin precedentes, sino también es de gran importancia para el desarrollo de las directrices de conservación de los ecosistemas. El momento de aparición (MdA), el punto en el que el cambio climático puede diferenciarse de un clima previo; es un concepto de aplicación extensa en los estudios de climatología y proporciona una estrategia sólida pero poco explorada para evaluar el riesgo del colapso ecosistémico, como está descrito por el criterio C de la Lista Roja de Ecosistemas (LRE) de la Unión Internacional para la Conservación de la Naturaleza. Identificamos las tres consideraciones teóricas del MdA para la evaluación de la LRE (grado de estabilidad, análisis multifactoriales en vez de unidimensionales y distintivos del colapso ecosistémico) y cuatro fuentes de incertidumbre cuando se aplica la metodología MdA (difusión intermodelo, periodo de referencia histórica, consenso entre las variables y consideración de escenarios distintos), la cual busca evitar el mal uso y los errores mientras se promueve una aplicación adecuada del marco de los científicos y lo practicantes. La incorporación del MdA a las evaluaciones de la LRE añade información importante para el establecimiento de prioridades de conservación que permiten la predicción de cambios dentro y más allá del marco temporal propuesto por la LRE.

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This study sought to explore the uses meanings and negotiation that female heads of household from low-income areas gave to the transferred money in the COVID health emergency period. Our specific interest is in the withdrawal of 10% of pension funds and the Emergency Family Income (IFE) due to the monetary relevance of both programs. Based on a 10th-month follow-up of 14 female heads of household from low-income areas of Santiago, Chile, this qualitative study examines how the participating women "mark," in Zelizer's sense, the money they received. Thus, we seek to account for how, based on the source of money, its forms of access and the amounts received, women determine how to use it and assign meaning to its value.

Cette étude a cherché à explorer les usages, les significations et les négociations que les femmes chefs de famille des zones à faibles revenus ont donné à l'argent transféré pendant la période d'urgence sanitaire COVID. Nous intéressons plus particulièrement au retrait de 10% des fonds de pension et au revenu familial d'urgence (IFE) en raison de l'importance monétaire de ces deux programmes. Basée sur un suivi de dix mois de 14 femmes chefs de famille issues de quartiers défavorisés de Santiago du Chili, cette étude qualitative examine la manière dont les femmes participantes "marquent", au sens de Zelizer, l'argent qu'elles ont reçu. Ainsi, nous cherchons à expliquer comment, en fonction de la source de l'argent, de ses formes d'accès et des montants reçus, les femmes déterminent comment l'utiliser et attribuent une signification à sa valeur.

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Pesticides have well-documented negative consequences to control crop pests, and natural predators are alternatives and can provide an ecosystem service as biological control agents. However, there remains considerable uncertainty regarding whether such biological control can be a widely applicable solution, especially given ongoing climatic variation and climate change. Here, we performed a meta-analysis focused on field studies with natural predators to explore broadly whether and how predators might control pests and in turn increase yield. We also contrasted across studies pest suppression by a single and multiple predators and how climate influence biological control. Predators reduced pest populations by 73% on average, and increased crop yield by 25% on average. Surprisingly, the impact of predators did not depend on whether there were many or a single predator species. Precipitation seasonality was a key climatic influence on biological control: as seasonality increased, the impact of predators on pest populations increased. Taken together, the positive contribution of predators in controlling pests and increasing yield, and the consistency of such responses in the face of precipitation variability, suggest that biocontrol has the potential to be an important part of pest management and increasing food supplies as the planet precipitation patterns become increasingly variable.

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The possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern1-3. For 65 million years, Amazonian forests remained relatively resilient to climatic variability. Now, the region is increasingly exposed to unprecedented stress from warming temperatures, extreme droughts, deforestation and fires, even in central and remote parts of the system1. Long existing feedbacks between the forest and environmental conditions are being replaced by novel feedbacks that modify ecosystem resilience, increasing the risk of critical transition. Here we analyse existing evidence for five major drivers of water stress on Amazonian forests, as well as potential critical thresholds of those drivers that, if crossed, could trigger local, regional or even biome-wide forest collapse. By combining spatial information on various disturbances, we estimate that by 2050, 10% to 47% of Amazonian forests will be exposed to compounding disturbances that may trigger unexpected ecosystem transitions and potentially exacerbate regional climate change. Using examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories, involving different feedbacks and environmental conditions. We discuss how the inherent complexity of the Amazon adds uncertainty about future dynamics, but also reveals opportunities for action. Keeping the Amazon forest resilient in the Anthropocene will depend on a combination of local efforts to end deforestation and degradation and to expand restoration, with global efforts to stop greenhouse gas emissions.

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Humans often face the challenge of making decisions between ambiguous options. The level of ambiguity in decision-making has been linked to activity in the parietal cortex, but its exact computational role remains elusive. To test the hypothesis that the parietal cortex plays a causal role in computing ambiguous probabilities, we conducted consecutive fMRI and TMS-EEG studies. We found that participants assigned unknown probabilities to objective probabilities, elevating the uncertainty of their decisions. Parietal cortex activity correlated with the objective degree of ambiguity and with a process that underestimates the uncertainty during decision-making. Conversely, the midcingulate cortex (MCC) encodes prediction errors and increases its connectivity with the parietal cortex during outcome processing. Disruption of the parietal activity increased the uncertainty evaluation of the options, decreasing cingulate cortex oscillations during outcome evaluation and lateral frontal oscillations related to value ambiguous probability. These results provide evidence for a causal role of the parietal cortex in computing uncertainty during ambiguous decisions made by humans.

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Introduction: Family members of patients admitted to an Intensive Care Unit present high uncertainty level due to not knowing what is happening and to not having clear details about the related events; therefore, interventions are required to allow modulating those levels. Objective: To evaluate the effect of an educational Nursing intervention compared to conventional care on the uncertainty of family members of patients hospitalized in an ICU. Materials and methods: An experimental study with a sample comprised by 132 relatives of patients admitted to an ICU, randomly distributed in four Solomon groups (33 in each group). The Nursing intervention based on the concepts of the Uncertainty in Illness Theory was applied to both experimental groups and devised under the Whittemore and Grey parameters with three moments: assessment; education about the relative's hospitalization in the ICU; and accompaniment. This was done with pre-assessments for two groups and post-assessments for the four groups, using the PPUS-FM Uncertainty Scale. The data were analyzed by means of descriptive statistics and respective non-parametric analyses. The study took into account the ethical principles in research. Results: The family members in the experimental groups presented a lower final uncertainty level when compared to the control groups, with a difference of 73.04 points and a p-value of 0.001. Discussion: Standardized interventions and under a theoretical model allow reducing uncertainty in relatives of patients in ICUs. Conclusions: The Nursing intervention based on the Uncertainty theory allows reducing uncertainty in relatives of patients hospitalized in an Intensive Care Unit.

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The central theme of this article is the way in which psychiatry physicians-in-training deal with uncertainty in the discussion of clinical cases in Mexico. Methodologically, it is approached from the field of clinical ethnography and the narrative interpretation of plots in performative actions where there are sequences of communicative exchanges. In this way, it focuses on a detailed description of situations where clinical cases are reviewed to decipher, explain, and understand intersubjective meanings in the face of the emergence of uncertainty, its management, and the implications on decisions and actions. The study finds that limitations within the field of psychiatry lie in the nosographic construction of disease and its translation into the diagnostic hypotheses made by clinicians, where there are wide margins of ambiguity. The strategies implemented in the face of uncertainty are use of drugs, the collegiate review of the case, and utilization of intuition as a spontaneous, preconscious daily practice. The specific case described here provides a microscopic observation of the complex scenarios in which uncertainty occurs in educational and teaching processes, clearly revealing how patient care is articulated. The narratives and their interpretation are materials for training/curriculum and psychiatric clinical practice.

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A convergent parallel mixed methods design with qualitative data collection embedded in a quasi-experimental study was developed to examine the potential of three modalities of preparation for hospital discharge of the families of children with chronic diseases in terms of uncertainty levels and management of the disease at home. Caregivers of these children were divided into three groups: two experimental groups and one control group. Two scales were applied: one measured family management, and the other evaluated uncertainties in relation to the disease. In addition, an in-depth interview was conducted. Wilcoxon's test and the integrated response index were used in data analysis to compare performance between the groups. Inductive thematic analysis was employed for the qualitative data. The data were integrated, comparing the groups before and after preparation for hospital discharge. Twenty-five family caregivers completed this study. Data integration showed that the intervention group, in which the families developed planning to prepare for discharge in a dialogical way with professionals, presented better perceptions regarding care management when compared to structured guide and usual care groups. Participation of families in planning for hospital discharge showed a reduction in uncertainties regarding the disease and better care management of children at home.

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BACKGROUND AND OBJECTIVE: Traditional assessment of patient response in mechanical ventilation relies on respiratory-system compliance and airway resistance. Clinical evidence has shown high variability in these parameters, highlighting the difficulty of predicting them before the start of ventilation therapy. This motivates the creation of computational models that can connect structural and tissue features with lung mechanics. In this work, we leverage machine learning (ML) techniques to construct predictive lung function models informed by non-linear finite element simulations, and use them to investigate the propagation of uncertainty in the lung mechanical response. METHODS: We revisit a continuum poromechanical formulation of the lungs suitable for determining patient response. Based on this framework, we create high-fidelity finite element models of human lungs from medical images. We also develop a low-fidelity model based on an idealized sphere geometry. We then use these models to train and validate three ML architectures: single-fidelity and multi-fidelity Gaussian process regression, and artificial neural networks. We use the best predictive ML model to further study the sensitivity of lung response to variations in tissue structural parameters and boundary conditions via sensitivity analysis and forward uncertainty quantification. Codes are available for download at https://github.com/comp-medicine-uc/ML-lung-mechanics-UQ RESULTS: The low-fidelity model delivers a lung response very close to that predicted by high-fidelity simulations and at a fraction of the computational time. Regarding the trained ML models, the multi-fidelity GP model consistently delivers better accuracy than the single-fidelity GP and neural network models in estimating respiratory-system compliance and resistance (R2∼0.99). In terms of computational efficiency, our ML model delivers a massive speed-up of ∼970,000× with respect to high-fidelity simulations. Regarding lung function, we observed an almost matched and non-linear behavior between specific structural parameters and chest wall stiffness with compliance. Also, we observed a strong modulation of airways resistance with tissue permeability. CONCLUSIONS: Our findings unveil the relevance of specific lung tissue parameters and boundary conditions in the respiratory-system response. Furthermore, we highlight the advantages of adopting a multi-fidelity ML approach that combines data from different levels to yield accurate and efficient estimates of clinical mechanical markers. We envision that the methods presented here can open the way to the development of predictive ML models of the lung response that can inform clinical decisions.