WTI, Brent or implied volatility index: Perspective of volatility spillover from oil market to Chinese stock market.

This study investigates the impact of oil market uncertainty on the volatility of Chinese sector indexes. We utilize commonly used realized volatility of WTI and Brent oil price along with the CBOE crude oil volatility index (OVX) to embody the oil market uncertainty. Based on the sample span from Mar 16, 2011 to Dec 31, 2019, this study utilizes vector autoregression (VAR) model to derive the impacts of the three different uncertainty indicators on Chinese stock volatilities. The empirical results show, for all sectors, the impact of OVX on sectors volatilities are more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices, especially after the Chinese refined oil pricing reform of March 27, 2013. That implies OVX is more informative than traditional WTI and Brent oil prices with respect to volatility spillover from oil market to Chinese stock market. This study could provide some important implications for the participants in Chinese stock market.

Uncertain Facts or Uncertain Values? Testing the Distinction Between Empirical and Normative Uncertainty in Moral Judgments.

People can be uncertain in their moral judgments. Philosophers have argued that such uncertainty can either refer to the underlying empirical facts (empirical uncertainty) or to the normative evaluation of these facts itself (normative uncertainty). Psychological investigations of this distinction, however, are rare. In this paper, we combined factor-analytical and experimental approaches to show that empirical and normative uncertainty describe two related but different psychological states. In Study 1, we asked N = 265 participants to describe a case of moral uncertainty and to rate different aspects of their uncertainty about this case. Across this wide range of moral scenarios, our items loaded onto three reliable factors: lack of information, unclear consequences, and normative uncertainty. In Study 2, we confirmed this factor structure using predefined stimulus material. N = 402 participants each rated eight scenarios that systematically varied in their degree of uncertainty regarding the consequences of the described actions and in the value conflict that was inherent to them. The empirical uncertainty factors were mainly affected by the introduction of uncertainty regarding consequences, and the normative uncertainty factor was mainly affected by the introduction of value conflict. Our studies provide evidence that the distinction between empirical and normative uncertainty accurately describes a psychological reality. We discuss the relevance of our findings for research on moral judgments and decision-making, and folk metaethics.

Oil price uncertainty, oil pricing reform, and corporate profitability: The case of China.

This study investigates the impact of oil price uncertainty (OPU) on corporate profitability in China, the world's largest crude oil consumer. Most importantly, we examine how the Chinese government's oil price reform affects this relationship. Using the yearly data of Chinese-listed companies, we find that the uncertainty of oil prices negatively affects corporate profitability but positively impacts operating expenses from 2007 to 2020. This finding holds after robust tests, including alternative profitability metrics and endogeneity model. Most interestingly, implementing the 2013 market-oriented oil pricing reform amplifies the adverse impact of OPU on corporate profitability owing to increased operating costs in the post-2013 period. Moreover, the detrimental effect of uncertain oil prices on corporate profitability is less prominent for large-capitalized companies. This research adds to the body of knowledge on the factors affecting corporate profitability by highlighting the volatility effect of oil prices and government pricing mechanisms. The results offer grounds for legislators and corporate managers to consider how to control the uncertainty surrounding oil price matters to ensure stable corporate profitability.

Exploring the neural underpinnings of chord prediction uncertainty: an electroencephalography (EEG) study.

Predictive processing in the brain, involving interaction between interoceptive (bodily signal) and exteroceptive (sensory) processing, is essential for understanding music as it encompasses musical temporality dynamics and affective responses. This study explores the relationship between neural correlates and subjective certainty of chord prediction, focusing on the alignment between predicted and actual chord progressions in both musically appropriate chord sequences and random chord sequences. Participants were asked to predict the final chord in sequences while their brain activity was measured using electroencephalography (EEG). We found that the stimulus preceding negativity (SPN), an EEG component associated with predictive processing of sensory stimuli, was larger for non-harmonic chord sequences than for harmonic chord progressions. Additionally, the heartbeat evoked potential (HEP), an EEG component related to interoceptive processing, was larger for random chord sequences and correlated with prediction certainty ratings. HEP also correlated with the N5 component, found while listening to the final chord. Our findings suggest that HEP more directly reflects the subjective prediction certainty than SPN. These findings offer new insights into the neural mechanisms underlying music perception and prediction, emphasizing the importance of considering auditory prediction certainty when examining the neural basis of music cognition.

US FDA's Dose Optimization Postmarketing Requirements and Commitments of Oncology Approvals and the Impact on Product Labels from 2010 to 2022: An Emerging Landscape from Traditional to Novel Therapies.

BACKGROUND: Dose optimization is a focal point of many US Food and Drug Administration (FDA) drug approvals. We sought to understand the impact of the FDA's Postmarketing Commitments/Postmarketing Requirements (PMCs/PMRs) on dose optimization and prescriber labeling for oncology drugs. METHODS: Publicly available information was aggregated for all FDA oncology drug approvals between January 1, 2010, and December 31, 2022. Study completion dates were compared to product labeling before and after PMC/PMR fulfillment dates to evaluate labeling changes associated with dose-related PMCs/PMRs. Data were analyzed individually (2010-2015 and 2016-2022) due to differences in available information. RESULTS: From 2010 to 2015, 14 of 42 (33.3%) new molecular entities (NMEs) had dose-related PMCs/PMRs, with 6 of 14 (42.9%) resulting in a relevant label change. From 2016 to 2022, of the 314 new or supplemental applications approved, 21 had dose-related PMCs/PMRs (6.7%), which trended upward over time; 71.4% of dose-related PMCs/PMRs were NMEs. Kinase inhibitors (KIs) and antibody/peptide drug conjugates (ADCs/PDCs) were the most affected drug classes. Ten of the 21 approvals with dose-related PMCs/PMRs fulfilled their dosing PMCs/PMRs, and 3 of the 10 (30%) had relevant label changes. CONCLUSION: Most dose-related PMRs/PMCs were issued for NMEs. Of these, KIs and ADCs/PDCs were highly represented, reflecting their novelty and greater uncertainty around their safety profile. PMC/PMR issuance broadly increased over time. With the implementation of the FDA's Project Optimus in 2021, it remains to be seen whether fewer dose-related PMCs/PMRs emerge in future due to enhanced dose optimization in the premarketing setting.

Learning from Non-Routine Events and Teamwork in Intensive Care Units: Challenges and Opportunities.

Patients admitted to intensive care units (ICUs) have profound and complex illnesses, often fraught with uncertainties in diagnoses, treatments, and care decisions. Clinicians often deviate from best practices to handle ICUs' myriad complexities and uncertainties. Non-routine events (NREs), defined as any aspect of care perceived by clinicians as deviations from optimal care, are latent and frequent safety threats that, if left unchecked, can be precursors to adverse events. Proper identification and analysis of NREs that represent latent safety threats have been proposed as a feasible and more effective approach for performance improvement than traditional root cause analysis for patient safety events. However, NRE studies to date have yet to show the holistic picture of NREs in the contexts of teamwork and time-dependent tasks that are frequently associated with NREs. NREs, an upstream interventional area to understand root causes, team performance, and human-computer interaction, still needs to be expanded. This article presents concepts of NREs, and the use of real-world data (RWD) and informatics methodology to investigate NREs in contexts and discusses the opportunities and challenges to enhance NREs research in teamwork and time-dependent tasks.

How clinicians recognise people who are dying: An integrative review.

BACKGROUND: Timely recognition of dying is important for high quality end-of-life care however, little is known about how clinicians recognise dying. Late recognition is common and can lead to futile treatment that can prolong or increase suffering and prevent a change in the focus of care. AIM: To explore how clinicians caring for dying people recognise that they are in the last days or hours of life, as well as the factors that influence the recognition of dying. DESIGN: A systematically constructed integrative review of the literature. METHODS: Medline, Scopus, Cumulative Index to Nursing and Allied Health Literature, PsycInfo and Allied and Complementary Medicine were searched in July 2022. Papers were included if they were original research, discussed how clinicians recognise dying, available in English language and published in 2012 or later. A constant comparison approach was applied to the analysis and synthesis of the literature. RESULTS: 24 papers met the inclusion criteria. There were 3 main categories identified: 'Clues and signals' refers to prompts and signs that lead a clinician to believe a person is dying, incorporating the sub-categories 'knowing the patient over time', and 'intuition and experience'. 'Recognition by others' is where clinicians come to recognise someone is dying through others. This can be through a change in the context of care such as a tool or care plan or by communication with the team. 'Culture, system and practice' refers to the cultural beliefs of a setting that influences awareness of dying and denial of death as a possibility and avoidance of naming death and dying directly. System and practice of the setting also impact on recognition of dying. This involves work pace and intensity, shift systems and timing of senior reviews of patients. Uncertainty and its impact on recognition of dying are evident throughout the findings of this review. The seeking of certainty and the absence of the possibility of dying contributes to late recognition of dying. DISCUSSION: Recognition of dying is a complex process that occurs over time, involving a combination of intuition and gathering of information, that is influenced by contextual factors. A culture where dying is not openly acknowledged or even named explicitly contributes to late recognition of dying. A shared language and consistent terminology for explicitly naming dying are needed. Uncertainty is intrinsic to the recognition of dying and therefore a shift to recognising the possibility of dying rather than seeking certainty is needed. REGISTRATION: (PROSPERO) CRD42022360900. Registered September 2022.

Addressing Patient Requests to Add Dietary Supplements to Their Cancer Care-A Suggested Approach.

Dietary supplements are widely utilized by cancer patients as part of a complementary and integrative approach to their healthcare. However, a significant portion of patients refrain from discussing their supplement use with their physicians, often due to the perceived indifference or negativity of their healthcare providers. This communication gap exposes patients to unreliable information sources and potential risks associated with uninformed supplementation. As the healthcare landscape evolves, there is an increasing recognition of the pivotal role that physicians play in guiding patients' healthcare decisions. A patient-centered perspective prioritizes the provision of evidence-based information tailored to the individual's needs. It advocates for open discussions about potential risks and fosters shared decision making, respecting patient autonomy. Additionally, this approach involves offering alternative options, documenting patient preferences, and ensuring ongoing support while coordinating with the healthcare team. To address these evolving needs, healthcare providers must adopt a transformative perspective, becoming expert guides who engage with their patients as informed and empowered participants. This revised approach emphasizes an open dialogue that balances presenting facts and acknowledging uncertainties surrounding dietary supplement use. Our narrative review of the literature underscores the importance of a practical approach, centered on transparent discussions and respect for patient autonomy. By following this approach, healthcare providers can empower patients to navigate the complexities of dietary supplement use within the context of cancer care, thereby safeguarding patient safety and overall well-being. Notably, our proposed tool highlights the utilization of reliable sources, the risk stratification of supplements, specific recommendations, and subsequent monitoring, providing a structured framework for informed decision making.

Mindfulness Fills in the Blank Spaces Left by Affective Uncertainty Uplifting Adaptive Behaviors.

Drawing on the integrative model of uncertainty tolerance, we aimed to investigate whether uncertainty relates to adaptive performance, at the within-person level. We argue that daily uncertainty at work will trigger negative affective reactions that, in turn, will minimize adaptive performance. Moreover, we focus on socio-cognitive mindfulness as a cross-level moderator of the indirect relationship of uncertainty on adaptive performance via negative affect. To capture changes in daily life and test our model, we conducted two diary studies across 5-working days: One with a sample of telecommuters (n = 101*5 = 505), and the other with a sample of non-telecommuters (n = 253*5 = 1,265). Study 1 took place between February and March of 2021 (during the mandatory confinement), and Study 2 occurred between April and May 2021 (out of the mandatory confinement). Both studies were conducted in Portugal. The multilevel results showed that at the day-level of analysis, uncertainty decreased adaptive performance through the enhanced negative affect. Moreover, at the person-level of analysis mindfulness moderated (a) the direct relationship of uncertainty to adaptive performance, and (b) the indirect relationship of uncertainty to adaptive performance via negative affect, in such a way that it became weaker when mindfulness was higher (multilevel-mediated moderation effect). This relation was different between Studies 1 and 2; that is, in Study 1, teleworkers who were high on mindfulness engaged in more adaptive performance when negative affect was high. In Study 2, adaptive performance significantly decreased, when negative affect was higher, even though this effect was weaker for mindful of individuals. The findings show that mindfulness helps to fill in the spaces of the affective uncertainty attenuating its detrimental effects.

Spirituality in the uncertainty of illness: the perspective of oncology patients.

OBJECTIVE: To analyze spirituality in the process of illness uncertainty in cancer patients. METHODS: This is a qualitative study, in which Merle Mishel's Theory of Uncertainty of Disease was used as a theoretical framework; and as a methodological reference, the stages of Bardin's Content Analysis. As a technique for obtaining information, a semi-structured interview was used. RESULTS: Spirituality in the uncertainty of the disease varies from patient to patient and acts in a unique way. They presented readaptation attitudes in their reports. The presence of spirituality in their lives acted as the main force mechanism to deal with the uncertainty of the disease, and this moment was called by Mishel "probabilistic thinking". CONCLUSION: Patients demonstrated readaptation attitudes in their reports, and spirituality acted as the main mechanism of strength to deal with uncertainty in the disease.

Managing uncertainty in forecasting health workforce demand using the Robust Workforce Planning Framework: the example of midwives in Belgium.

BACKGROUND: In Belgium, the Planning Commission for Medical Supply is responsible for monitoring human resources for health (HRH) and ultimately proposing workforce quotas. It is supported by the Planning Unit for the Supply of the Health Professions. This Unit quantifies and forecasts the workforce in the healthcare professions on the basis of a stock and flow model, based on trends observed in the past. In 2019, the Planning Unit asked the KCE (Belgian Health Care Knowledge Centre) to develop additional forecasting scenarios for the midwifery workforce, to complement the standard historical trend approach. The aim of this paper is to present the development of such forecasting scenarios. METHODS: The Robust Workforce Planning Framework, developed by the Centre for Workforce Intelligence in the UK was used to develop alternative midwifery workforce scenarios. The framework consists of four steps (Horizon scanning, Scenario generation, Workforce modelling, and Policy analysis), the first two of which were undertaken by KCE, using two online surveys and five workshops with stakeholders. RESULTS: Three alternative scenarios are proposed. The first scenario (close to the current situation) envisages pregnancy and maternity care centred on gynaecologists working either in a hospital or in private practice. The second scenario describes an organisation of midwife-led care in hospitals. In the third scenario, care is primarily organised by primary care practitioners (midwives and general practitioners) in outpatient settings. CONCLUSIONS: The Robust Workforce Planning Framework provides an opportunity to adjust the modelling of the health workforce and inform decision-makers about the impact of their future decisions on the health workforce.

The dependence and risk spillover between economic uncertainties and the crude oil market: new evidence from a Copula-CoVaR approach incorporating the decomposition technique.

Understanding the risk spillover of the oil market in economic uncertainty is of great importance. However, it is difficult to take on a traditional single perspective in describing the risk spillover law of economic uncertainty in the crude oil market on different timescales. In order to fill the research gap resulting from such difficulty, this paper incorporates empirical mode decomposition into the time-varying Copula-CoVaR model, and for the first time explores the risk spillover path of economic uncertainty on the two international crude oil pricing benchmarks-Brent and West Texas Intermediate crude oil prices-using different timescales. The empirical results not only verify the necessity of research from the perspective of different timescales, but also reveal the heterogeneity of the risk spillover paths of different types of economic uncertainty on crude oil prices. The research in this paper provides a multi-perspective interpretation for understanding the complex risk spillovers between various economic uncertainties and the crude oil market, as well as providing meaningful information to support stakeholders in making rational decisions.

Determining the Relationship Between Intolerance of Uncertainty and Attitudes Toward Complementary and Alternative Medicine in Patients With Cancer.

Disease-related uncertainty and the factors associated with uncertainty in patients with cancer have not been adequately investigated. This study aimed to determine the relationship between intolerance of uncertainty and attitudes toward complementary and alternative medicine (CAM) in patients with cancer. This cross-sectional descriptive study included 351 patients treated in the oncology clinic of a university hospital. All participants completed the Holistic Complementary and Alternative Medicine Questionnaire (HCAMQ) and the 12-item Intolerance of Uncertainty Scale (IUS-12). Most patients (84.3%) said that they had information about CAM, 39.7% reported obtaining this information from their social environment, 83.2% said that they believed that CAM methods are effective, and 76.0% reported not telling health care professionals about their CAM use. Mean IUS-12 and HCAMQ total scores were 46.53 ± 2.05 and 32.11 ± 2.32, respectively, and showed a weak negative correlation. The patients in our study showed high intolerance of uncertainty and a moderately positive attitude toward CAM, and most patients did not notify health care professionals of their CAM use. Therefore, we recommend assessing intolerance of uncertainty and CAM use in this patient group. By identifying patients experiencing uncertainty and seeking different treatment, nurses can support patients physiologically, psychologically, and socially, and can explain the effects of CAM use.

Robust sustainable canola oil-based biodiesel supply chain network design under supply and demand uncertainty.

The excessive consumption of fossil fuels has sparked debates and caused environmental damage, leading the global community to search for a suitable alternative. To achieve sustainable development goals and prevent harmful climate scenarios, the world needs to increase its use of renewable energy. Biodiesel, a clean and eco-friendly fuel with a high flash point and more lubrication than petroleum-based fuels, and without the emission of harmful environmental gases, has emerged as one of the fossil fuel alternatives. To promote the mass-level production of biodiesel, a sustainable supply chain (SC) that does not depend on laboratory production is necessary. For this purpose, this research proposes a multi-objective mixed-integer non-linear mathematical programming (MINLP) model to design a sustainable canola oil-based biodiesel supply chain network (CO-BSCND) under supply and demand uncertainty. This mathematical model aims to minimize the total cost (TC) and total carbon emission while maximizing the total number of job opportunities simultaneously. A scenario-based robust optimization (SBRO) approach is applied to deal with uncertainty. The proposed model is implemented in a real case study in Iran, and numerical experiments and sensitivity analysis are conducted to demonstrate its applicability. The results of this research demonstrate that designing a sustainable supply chain network for the production and distribution of biodiesel fuel is achievable. Moreover, this mathematical modeling makes mass-scale production of biodiesel fuel a possibility. In addition, the SBRO method adopted in this research enables managers and researchers to explore the design conditions of the supply chain network by controlling the uncertainties that affect it. This approach allows the chain's performance to be as close as possible to the actual conditions. As a result, the SBRO method enhances the efficiency of the supply chain network and boosts productivity toward achieving desired goals.

What affects China's green finance? Evidence from cryptocurrency market, oil market, and economic policy uncertainty.

This paper discusses the dynamic long- and short-term effects of bitcoin price (BTP), crude oil price (COP), and uncertainty of economic policy (EPU) on China's green bond (CGB) market, separately. Depending on the quantile autoregressive distributed lag method, the empirical results are shown that BTP and EPU exert substantial positive and negative effects on the CGB market in the long term for most circumstances, while their effects reflect not prominent in the short term. The main contributions can be summarized as follows. Given that China is the largest bitcoin mining state and a major green bond issuer, this study first explores the linkages between them. Furthermore, both long- and short-term effects are investigated from BTP, COP, and EPU to CGB, and long-term effects are dominated in the interrelationships among variables, indicating that the CGB market is mainly driven by permanent shocks. In addition, the mentioned long-term effects are deeply discussed from time- and quantile-varying aspects. This approach considers diverse situations in the bond market and various incidents that occur at various durations of time. The results underscore the significance of market participants gaining a deeper comprehension of how BTP, COP, and EPU impact green bond within varying market conditions. Implementing specific policies, such as establishing a cohesive and efficient bond market and making careful adjustments to economic policies, can be advantageous in maintaining stability within the CGB market.

The role of critical care nurses in shared decision-making for patients with severe heart failure: A qualitative study.

AIM: Patients with severe heart failure undergo highly invasive and advanced therapies with uncertain treatment outcomes. For these patients, shared decision-making is necessary. To date, the nursing perspective of the decision-making process for patients facing difficulties and how nurses can support patients in this process have not been fully elucidated. This study aimed to clarify the perceptions of critical care nurses regarding situations with patients with severe heart failure that require difficult decision-making, and their role in supporting these patients. METHODS: Individual semi-structured interviews were conducted with 10 certified nurse specialists in critical care nursing at nine hospitals in Japan. A qualitative inductive method was used and the derived relationships among the themes were visually structured and represented. RESULTS: The nurses' perceptions on patients' difficult situations in decision-making were identified as follows: painful decisions under uncertainties; tense relationships; wavering emotions during decision-making; difficulties in coping with worsening medical conditions; patients' wishes that are difficult to realize or estimate; and difficulties in transitioning from advanced medical care. Critical care nurses' roles were summarized into six themes and performed collaboratively within the nursing team. Of these, the search for meaning and value was fundamental. Two positions underpin the role of critical care nurses. The first aims to provide direct support and includes partnerships and rights advocacy. The second aims to provide a holistic perspective to enable necessary adjustments, as indicated by situation assessments and mediation. By crossing various boundaries, co-creating, and forming a good circular relationship in the search for meaning and values, the possibility of expanding treatment and recuperation options may be considered. CONCLUSIONS: Patients with severe heart failure have difficulty participating in shared decision-making. Critical care nurses should collaborate within the nursing team to improve interprofessional shared decision-making by providing decisional support to patients that focuses on values and meaning.

Probabilistic double hierarchy linguistic Maclaurin symmetric mean-MultiCriteria Border Approximation area Comparison method for multi-criteria group decision making and its application in a selection of traditional Chinese medicine prescriptions.

Traditional Chinese medicine (TCM) has gradually played an indispensable role in people's health maintenance, especially in the treatment of chronic diseases. However, there is always uncertainty and hesitation in the judgment and understanding of diseases by doctors, which affects the status recognition and optimal diagnosis and treatment decision-making of patients. In order to overcome the above problems, we lead into probabilistic double hierarchy linguistic term set (PDHLTS) to accurately describe language information in traditional Chinese medicine and make decisions. In this paper, a multi-criteria group decision making (MCGDM) model is constructed based on the MSM-MCBAC (Maclaurin symmetric mean-MultiCriteria Border Approximation area Comparison) method in the PDHL environment. Firstly, a PDHL weighted Maclaurin symmetric mean (PDHLWMSM) operator is proposed to aggregate the evaluation matrices of multiple experts. Then, combined with the BWM and maximizing deviation method, a comprehensive weight determination method is put forward to calculate the weights of criteria. Furthermore, we propose PDHL MSM-MCBAC method based on the Multi-Attributive Border Approximation area Comparison (MABAC) method and the PDHLWMSM operator. Finally, an example of a selection of TCM prescriptions is used and some comparative analyses are made to verify the effectiveness and superiority of this paper.

A Bayesian approach to predictive uncertainty in chemotherapy patients at risk of acute care utilization.

BACKGROUND: Machine learning (ML) predictions are becoming increasingly integrated into medical practice. One commonly used method, ℓ1-penalised logistic regression (LASSO), can estimate patient risk for disease outcomes but is limited by only providing point estimates. Instead, Bayesian logistic LASSO regression (BLLR) models provide distributions for risk predictions, giving clinicians a better understanding of predictive uncertainty, but they are not commonly implemented. METHODS: This study evaluates the predictive performance of different BLLRs compared to standard logistic LASSO regression, using real-world, high-dimensional, structured electronic health record (EHR) data from cancer patients initiating chemotherapy at a comprehensive cancer centre. Multiple BLLR models were compared against a LASSO model using an 80-20 random split using 10-fold cross-validation to predict the risk of acute care utilization (ACU) after starting chemotherapy. FINDINGS: This study included 8439 patients. The LASSO model predicted ACU with an area under the receiver operating characteristic curve (AUROC) of 0.806 (95% CI: 0.775-0.834). BLLR with a Horseshoe+ prior and a posterior approximated by Metropolis-Hastings sampling showed similar performance: 0.807 (95% CI: 0.780-0.834) and offers the advantage of uncertainty estimation for each prediction. In addition, BLLR could identify predictions too uncertain to be automatically classified. BLLR uncertainties were stratified by different patient subgroups, demonstrating that predictive uncertainties significantly differ across race, cancer type, and stage. INTERPRETATION: BLLRs are a promising yet underutilised tool that increases explainability by providing risk estimates while offering a similar level of performance to standard LASSO-based models. Additionally, these models can identify patient subgroups with higher uncertainty, which can augment clinical decision-making. FUNDING: This work was supported in part by the National Library Of Medicine of the National Institutes of Health under Award Number R01LM013362. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Combination of expert guidelines-based and machine learning-based approaches leads to superior accuracy of automated prediction of clinical effect of copy number variations.

Clinical interpretation of copy number variants (CNVs) is a complex process that requires skilled clinical professionals. General recommendations have been recently released to guide the CNV interpretation based on predefined criteria to uniform the decision process. Several semiautomatic computational methods have been proposed to recommend appropriate choices, relieving clinicians of tedious searching in vast genomic databases. We have developed and evaluated such a tool called MarCNV and tested it on CNV records collected from the ClinVar database. Alternatively, the emerging machine learning-based tools, such as the recently published ISV (Interpretation of Structural Variants), showed promising ways of even fully automated predictions using broader characterization of affected genomic elements. Such tools utilize features additional to ACMG criteria, thus providing supporting evidence and the potential to improve CNV classification. Since both approaches contribute to evaluation of CNVs clinical impact, we propose a combined solution in the form of a decision support tool based on automated ACMG guidelines (MarCNV) supplemented by a machine learning-based pathogenicity prediction (ISV) for the classification of CNVs. We provide evidence that such a combined approach is able to reduce the number of uncertain classifications and reveal potentially incorrect classifications using automated guidelines. CNV interpretation using MarCNV, ISV, and combined approach is available for non-commercial use at https://predict.genovisio.com/ .