Standard Decision Research Articles

Non-standard monetary policy and ECB communication: Confusion or predictability?

Using a dataset of 273 press releases and introductory statements over the period 1999-2020, we investigate how the communication of the European Central Bank (ECB) influences the ability of market participants to predict its monetary decisions. We build four new indicators of communication intensity and frequency and measure predictability by decomposing the surprises on the variation of asset prices around monetary announcements into a “target” – which captures the standard dimension of monetary policy surprises – a “path” factor – which corresponds to the forward guidance dimension of monetary surprises – and a QE factor – which accounts for their quantitative easing dimension. Our findings reveal that the intensity and frequency of ECB communication reduce the surprise of market participants regarding non-standard dimensions of monetary policy. Moreover, the improvement of the predictability of monetary decisions is not achieved at the expense of greater confusion over standard monetary decisions. Additionally, the reduction in surprises operates through the information channel, by which monetary policy announcements transfer information about economic fundamentals from the central bank to market participants.

Patient trust in the use of machine learning-based clinical decision support systems in psychiatric services: A randomized survey experiment.

Clinical decision support systems (CDSS) based on machine-learning (ML) models are emerging within psychiatry. If patients do not trust this technology, its implementation may disrupt the patient-clinician relationship. Therefore, the aim was to examine whether receiving basic information about ML-based CDSS increased trust in them. We conducted an online randomized survey experiment in the Psychiatric Services of the Central Denmark Region. The participating patients were randomized into one of three arms: Intervention=information on clinical decision-making supported by an ML model; Active control=information on a standard clinical decision process, and Blank control=no information. The participants were unaware of the experiment. Subsequently, participants were asked about different aspects of trust and distrust regarding ML-based CDSS. The effect of the intervention was assessed by comparing scores of trust and distrust between the allocation arms. Out of 5800 invitees, 992 completed the survey experiment. The intervention increased trust in ML-based CDSS when compared to the active control (mean increase in trust: 5% [95% CI: 1%; 9%], p=0.0096) and the blank control arm (mean increase in trust: 4% [1%; 8%], p=0.015). Similarly, the intervention reduced distrust in ML-based CDSS when compared to the active control (mean decrease in distrust: -3%[-1%; -5%], p=0.021) and the blank control arm (mean decrease in distrust: -4% [-1%; -8%], p=0.022).No statistically significant differences were observed between the active and the blank control arms. Receiving basic information on ML-based CDSS in hospital psychiatry may increase patient trust in such systems.

A comparison of imprecise Bayesianism and Dempster–Shafer theory for automated decisions under ambiguity

Abstract Ambiguity occurs insofar as a reasoner lacks information about the relevant physical probabilities. There are objections to the application of standard Bayesian inductive logic and decision theory in contexts of significant ambiguity. A variety of alternative frameworks for reasoning under ambiguity have been proposed. Two of the most prominent are Imprecise Bayesianism and Dempster–Shafer theory. We compare these inductive logics with respect to the Ambiguity Dilemma, which is a problem that has been raised for Imprecise Bayesianism. We develop an agent-based model comparison that isolates the difference between the two inductive logics in their updating methods. We find that Dempster–Shafer theory does not avoid the Ambiguity Dilemma. We discuss the implications of this result.

An Attribution of Value Framework for Combination Treatments

ObjectivesThe use of cost-effectiveness methods to support policy decisions has become well established, but difficulties can arise when evaluating a new treatment that is indicated to be used in combination with an established backbone treatment. If the latter has been priced close to the decision maker’s willingness-to-pay threshold, this may mean that there is no headroom for the new treatment to demonstrate value, at any price, even if the combination is clinically effective. Without a mechanism for attributing value to component treatments within a combination therapy, the health system risks generating negative funding decisions for combinations of proven clinical benefit to patients. The aim of this work was to define a value attribution methodology, which could be used to allocate value between the components of any combination treatment. MethodsThe framework is grounded in the standard decision rules of cost-effectiveness analysis and provides solutions according to key features of the problem: perfect/imperfect information about component treatment monotherapy effects and balanced/unbalanced market power between their manufacturers. ResultsThe share of incremental value varies depending on whether there is perfect/imperfect information and balance/imbalance of market power, with some scenarios requiring the manufacturers to negotiate a share of the incremental value within a range defined by the framework. ConclusionsIt is possible to define a framework that is independent of price and focuses on benefits expressed as quality-adjusted life-year gains (and/or quality-adjusted life-year equivalents for cost savings), a standard metric used by many health technology assessment agencies to evaluate novel treatments.

Integrating autoencoder and decision tree models for enhanced energy consumption forecasting in microgrids: A meteorological data-driven approach in Djibouti

At this time, as the world and nations move to reduce the use of fossil fuels, research is oriented toward improving the energy consumption of people and buildings. Recent methods, mainly computing techniques such as deep learning, are proposed in the literature. This paper proposes a model that integrates the autoencoder with the decision tree model using six months of meteorological data, including solar radiation, wind speed, temperature, and other meteorological data. This study aims to advance the energy consumption prediction of loads connected to the microgrid. A case study of a microgrid park of a campus in Djibouti is presented to test the proposed model. Autoencoders were exploited to extract the main features of the meteorological data. Then the decision tree is proposed to predict the energy consumption using the resulting encoded features. The evaluation of the training of the autoencoder model gave a favorable result with a mean squared error of 0.002 and an R2 value of 0.99. Hyperparameter tuning scenarios facilitated the exploration of the decision tree model. Ensemble decision trees performed better than individual trees in this model, achieving a mean absolute error of 1.4 % and an R2 value of 0.997. It showed that hyperparameter tuning improved the results due to the best architecture fit for the decision tree. Moreover, the proposed model was validated by comparing it with the literature on KNN, SVR, and MLP models commonly used in microgrid energy management. The autoencoder decision tree outperformed other compared methods, achieving an explained variance of 0.997 and an MAE of 1.7 % in the standard decision tree regressor scenario. These results demonstrated the capabilities of machine learning and weather data. Combining an autoencoder and the decision tree model will open a new door to energy management improvement.

Multi-criteria group decision analytics for sustainable supplier relationship management in a focal manufacturing company

In the context of sustainable development, while the benefits of incorporating environmental, social, and governance (ESG) considerations into supplier management have been widely discussed in recent years, a significant gap in the literature remains. Specifically, there is a lack of a standardized decision analytics framework for ESG-oriented supplier relationship management (SRM) processes in the manufacturing sector. To address this gap, this study introduces a multi-criteria supplier analytics framework (MCSAF) towards sustainable SRM. Through referring traditional supplier evaluation criteria as well as emerging ESG criteria, a novel decision hierarchy for supplier segmentation is developed. The proposed system utilizes the Bayesian best-worst method and the Canopy-k-means clustering algorithm to formulate different supplier segments for strategic development. The proposed MCSAF has been successfully implemented in a German solar water pump manufacturer. The results show that the five highest-ranked criteria are ‘Quality’, ‘Impact and importance on product quality and business growth’, ‘Environmental policy/management’, ‘Customer and product responsibility’, and ‘Risk and crisis management’. 52 suppliers are categorized into five segments, leading to the formulation of ESG-enabled supplier development strategies are subsequently formulated. The successful implementation of the MCSAF showcases its effectiveness in advancing ESG practices in sustainable supplier management.

Experience of an interdisciplinary management for pediatric Moyamoya disease: application of a novel Hemispheric Surgical Score.

The primary purpose of this study was to develop and implement a novel Hemispheric Surgical Score to guide the treatment of pediatric patients with Moyamoya disease (MMD). Additionally, we aimed to describe a comprehensive flowchart for the evaluation, treatment, and follow-up of these patients and to share our experience with the interdisciplinary management of a large pediatric cohort at a referral pediatric hospital. We conducted a retrospective observational study using medical records of patients diagnosed with MMD at the Pediatric Hospital "Prof. Dr. Juan P. Garrahan" in Buenos Aires, Argentina, from July 2013 to July 2023. From July 2016 onward, data were analyzed prospectively following the implementation of the Hemispheric Surgical Score and the flowchart. Evaluations included clinical, MRI, and angiographic criteria, and patients were managed by an interdisciplinary team. Demographic, clinical, and neuroimaging data were collected and analyzed. Eighty hemispheres from 40 patients were analyzed, with cerebral revascularization performed on 72 hemispheres from 37 patients. The Hemispheric Surgical Score and flowchart standardized treatment decisions, and reduced the need for invasive studies like angiographies for follow-up. The majority of patients (79.1%) had favorable outcomes, with complete disease progression arrest and no worsening of imaging nor clinical scores during a median follow-up of 35.8months. The Hemispheric Surgical Score and the comprehensive flowchart have improved the management of MMD in pediatric patients by standardizing treatment and reducing unnecessary invasive procedures. This interdisciplinary approach has led to better patient outcomes, highlighting the need for further validation in larger studies and comparisons of different revascularization techniques through randomized clinical trials.

Gut Microbiota-Related Biomarkers in Immuno-Oncology.

Carcinogenesis is associated with the emergence of protracted intestinal dysbiosis and metabolic changes. Increasing evidence shows that gut microbiota-related biomarkers and microbiota-centered interventions are promising strategies to overcome resistance to immunotherapy. However, current standard methods for evaluating gut microbiota composition are cost- and time-consuming. The development of routine diagnostic tools for intestinal barrier alterations and dysbiosis constitutes a critical unmet medical need that can guide routine treatment and microbiota-centered intervention decisions in patients with cancer. In this review, we explore the influence of gut microbiota on cancer immunotherapy and highlight gut-associated biomarkers that have the potential to be transformed into simple diagnostic tools, thus guiding standard treatment decisions in the field of immuno-oncology. Mechanistic insights toward leveraging the complex relationship between cancer immunosurveillance, gut microbiota, and metabolism open exciting opportunities for developing novel biomarkers in immuno-oncology.

Automated monitoring of tooth wear progression using AI on intraoral scans

ObjectivesThis study aimed to develop and evaluate a fully automated method for visualizing and measuring tooth wear progression using pairs of intraoral scans (IOSs) in comparison with a manual protocol. MethodsEight patients with severe tooth wear progression were retrospectively included, with IOSs taken at baseline and 1-year, 3-year, and 5-year follow-ups. For alignment, the automated method segmented the arch into separate teeth in the IOSs. Tooth pair registration selected tooth surfaces that were likely unaffected by tooth wear and performed point set registration on the selected surfaces. Maximum tooth profile losses from baseline to each follow-up were determined based on signed distances using the manual 3D Wear Analysis (3DWA) protocol and the automated method. The automated method was evaluated against the 3DWA protocol by comparing tooth segmentations with the Dice-Sørensen coefficient (DSC) and intersection over union (IoU). The tooth profile loss measurements were compared with regression and Bland-Altman plots. Additionally, the relationship between the time interval and the measurement differences between the two methods was shown. ResultsThe automated method completed within two minutes. It was very effective for tooth instance segmentation (826 teeth, DSC = 0.947, IoU = 0.907), and a correlation of 0.932 was observed for agreement on tooth profile loss measurements (516 tooth pairs, mean difference = 0.021mm, 95% confidence interval = [-0.085, 0.138]). The variability in measurement differences increased for larger time intervals. ConclusionsThe proposed automated method for monitoring tooth wear progression was faster and not clinically significantly different in accuracy compared to a manual protocol for full-arch IOSs. Clinical significanceGeneral practitioners and patients can benefit from the visualization of tooth wear, allowing quantifiable and standardized decisions concerning therapy requirements of worn teeth. The proposed method for tooth wear monitoring decreased the time required to less than two minutes compared with the manual approach, which took at least two hours.

Reducing Failed Extubations in Preterm Infants Via Standardization and Real-Time Decision Support.

Failed extubations are associated with pulmonary morbidity in hospitalized premature newborns. The objective of this study was to use quality improvement methodology to reduce failed extubations through practice standardization and integrating a real-time extubation success calculator into the electronic medical record (EMR). A specific, measurable, achievable, relevant, and time-bound aim was developed to reduce failed extubations (defined as reintubation <5 days from primary extubation) by 50% among infants <32 weeks' gestational age (GA) or <1500 g birth weight by December 31, 2022. Plan-do-study-act cycles were developed to standardize postextubation respiratory support and integrate the EMR-based calculator. Outcome measures included extubation failure rates. Balancing measures included days on mechanical ventilation and number of patients intubated <3 days. Process measures were followed for guideline compliance. Statistical process control charts were used to track time-ordered data and detect special cause variation. We observed a reduction in failed extubations from 10.3% to 2.3%, with special cause variation noted after both plan-do-study-act cycle #1 and #2. Special cause variation was detected in both GA subgroups: <28 weeks' GA (22.0%-8.6%) and ≥28 weeks' GA (4.6%-0.3%). Additionally, the average number of infants intubated <3 days increased (60.2%-73.6%), whereas average ventilator days decreased (10.8-7.0). Finally, the time from infants' extubation score reaching threshold (≥60%) to extubation decreased (14.1-6.4 days) after launching the EMR-integrated calculator. Practice standardization and implementation of an EMR-based real-time clinical decision support tool improved extubation success, promoted earlier extubation, and reduced ventilator days in premature newborns.

Fast linear model trees by PILOT

Linear model trees are regression trees that incorporate linear models in the leaf nodes. This preserves the intuitive interpretation of decision trees and at the same time enables them to better capture linear relationships, which is hard for standard decision trees. But most existing methods for fitting linear model trees are time consuming and therefore not scalable to large data sets. In addition, they are more prone to overfitting and extrapolation issues than standard regression trees. In this paper we introduce PILOT, a new algorithm for linear model trees that is fast, regularized, stable and interpretable. PILOT trains in a greedy fashion like classic regression trees, but incorporates an L2 boosting approach and a model selection rule for fitting linear models in the nodes. The abbreviation PILOT stands for PIecewise Linear Organic Tree, where ‘organic’ refers to the fact that no pruning is carried out. PILOT has the same low time and space complexity as CART without its pruning. An empirical study indicates that PILOT tends to outperform standard decision trees and other linear model trees on a variety of data sets. Moreover, we prove its consistency in an additive model setting under weak assumptions. When the data is generated by a linear model, the convergence rate is polynomial.

Model‐Based Decision Support using Test and Evaluation: A Lightweight Architecture Approach

AbstractA standardized decision support tool to support test prioritization and decision‐making is a prerequisite to achieving on‐time delivery of weapon systems that are adequately tested and vetted by decision makers within distributed organizations such as the defense industrial base. However, to achieve standardization, the core principles of systems thinking and systems engineering must be utilized in order to realize a holistic and evolvable process and product that integrates Test &amp; Evaluation (T&amp;E) data and information with decision‐making in a consistent format to support program offices. To address this need, a model‐based reference architecture for the standardization of the Integrated Decision Support Key (IDSK) data and decision formats is presented in this paper. The IDSK is a DoD‐mandated decision support tool (artifact) for capturing a program's decisions and the T&amp;E information necessary to support the decisions. The digital‐IDSK reference architecture proposed is developed using a model‐based systems engineering approach and facilitates the generation of standardized data and decision formats that are tailorable, adaptable to incremental future changes, and easy to integrate with other digital engineering artifacts all within a digital engineering ecosystem. In addition, this approach is also applicable not only to defense‐based organizations, but also finds utility within organizations pivoting to a digital engineering strategy for informing product T&amp;E decision‐making.

Improving Evidence-based Management of Children and Adolescents with Elevated Blood Pressure during a Well-Child Encounter: A Quality Initiative

INTRODUCTION: Pediatric hypertension requires accurate diagnosis and management to reduce the burden of cardiovascular disease across the lifespan. Despite national guidelines, hypertension (HTN) is one of the most frequently missed diagnoses within pediatric primary care. This quality improvement (QI) project aimed to improve the management of patients aged 3–18 years with elevated blood pressure (BP) by 25% during a well-child encounter. METHODS: Over one year, a single-center QI project was conducted at an academic general pediatrics center. Multidisciplinary interventions, including team education, workflow standardization, and clinical decision support tools, were implemented using Plan-Do-Study-Act cycles to improve adherence to evidence-based guidelines. Outcome measures were the percentage of youth with an elevated BP on screening who had documentation of provider actions. We used run charts to analyze the effect of interventions, and descriptive statistics for demographic data and process measures pre- and postintervention. Chi-square tests compared process measures pre- and postintervention and provider actions stratified by age. RESULTS: Key drivers of change were the percentage of documented confirmatory BPs, BP stage, and scheduled follow-up appointments. At baseline, 13% of children with elevated BP on screening had guideline-adherent provider actions documented. This increased to 40% postintervention; however, discrepancies in adherence stratified by age were observed. Youth 13 and older were significantly more likely to receive a provider action than younger children (P &lt; 0.0001). CONCLUSION: BP management during a well-child encounter improved with multidisciplinary interventions that increased knowledge of evidence-based guidelines and standardized care.

Use of standardized decision support instruments to inform child welfare decision-making: lessons from an implementation study

Research has demonstrated children in out-of-home care have experienced trauma and a significant proportion are in need of behavioral health services (e.g. Casaneuva et al., NSCAW II baseline report: Child well-being, US Department of Health and Human Services, Washington, DC, 2011). Accessing services requires interagency coordination between child welfare and behavioral health professionals; however, challenges to coordination and collaboration may result in lack of service utilization for many youth (Hanson et al. 2016). Utilizing a mixed methodological approach, this paper describes the results of a study conducted five years after full state-wide implementation of processes designed to promote the use of evidence-based practices to inform decision-making for youth dually served by the child welfare and behavioral health systems. Outcomes from the study were used to develop strategies to address programmatic concerns and reinforce implementation supports. Study findings may aid organizations seeking to reinforce data-informed practices and employ strategies for addressing barriers at the worker and agency level.

Desafiando e distinguindo precedentes e padrões decisórios: o caso da progressão de servidor público e dos limites da Lei de Responsabilidade Fiscal (RESP 1878849)

This article studies interpretative paradigms in law, especially precedents and standard decisions. Special Appeal-RESP No. 1878849 was selected, in which it is argued that the functional progressions of civil servants are not prevented when personnel expenses exceed the limits of the Fiscal Responsibility Law, for the hermeneutical exercise of demonstrating that a judicial manifestation can be both a precedent and a standard decision, depending on the context of subsequent cases. The general objective is to contribute to understanding the operationalization of these interpretative paradigms, based on Law as Integrity. The problem addressed is: what challenges and distinctions could RESP No. 1878849 be subjected to, considering that decision as a binding standard decision in the first case and precedent in the second? It was concluded that paradigms can be challenged, and it is relevant to think about ways of dealing with an instrument created to prevail in the face of its formally binding effectiveness, such as the standard decision. In addition, the paradigms studied are distinguishable, but the standard decision formed in a repetitive special appeal is aimed at identical legal issues (so that the absence of identity indicates a distinction), while the precedent requires a more sophisticated hermeneutical effort.

Closed paths in graphs vs. voting theory

Features of graphs that hinder finding closed paths with particular properties, as represented by the Traveling Salesperson Problem—TSP, are identified for three classes of graphs. Removing these terms leads to a companion graph with identical closed path properties that is easier to analyze. A surprise is that these troubling graph factors are precisely what is needed to analyze certain voting methods, while the companion graph’s terms are what cause voting theory complexities as manifested by Arrow’s Theorem. This means that the seemingly separate goals of analyzing closed paths in graphs and analyzing voting methods are complementary: components of data terms that assist in one of these areas are the source of troubles in the other. Consequences for standard decision methods are in Sects. 2.5, 3.7 and the companion paper (Saari in Theory Decis 91(3):377–402, 2021). The emphasis here is on paths in graphs; incomplete graphs are similarly handled.

Barriers and Facilitators of Using R for Decision Analytic Modeling in Health Technology Assessment: Focus Group Results.

Decision models for health technology assessment (HTA) are largely submitted to HTA agencies using commercial software, which has known limitations. The use of the open-source programming language R has been suggested because of its efficiency, transparency, reproducibility, and ability to consider complex analyses. However, its use in HTA remains limited. This qualitative study aimed to explore the main reasons for this slow uptake of R in HTA and identify tangible facilitators. We undertook two semi-structured focus group discussions with 24 key stakeholders from government agencies, consultancy, pharmaceutical companies, and academia. Two 1.5-hour discussions reflected on barriers identified in a previous study and highlighted additional barriers. Discussions were recorded and semi-transcribed, and data were organized and summarized into key themes. Human resources constraints were identified as a key barrier, including a lack of training, prioritization and collaboration, and resistance to change. Another key barrier was the lack of acceptance, or clear guidance, around submissions in R by HTA agencies. Participants also highlighted a lack of communication around accepted packages and decision model structures, and between HTA agencies on standard decision modeling structures. There is a need for standardization, which can facilitate decision model sharing, coding homogeneity, and improved country adaptations. The creation of training materials and tailored workshops was identified as a key short-term facilitator. Increased communication and engagement of stakeholders could also facilitate the use of R by identifying needs and opportunities, encouraging HTA agencies to address structural barriers, and increasing incentives to use R.

Current Management of Acute and Posttraumatic Critical Bone Defects.

Limb reconstruction in patients with critical-sized bone defects remains a challenge due to the availability of various technically demanding treatment options and a lack of standardized decision algorithms. Although no consensus exists, it is apparent from the literature that the combination of patient, surgeon, and institutional collaborations is effective in providing the most efficient care pathway for these patients. Success relies on choosing a particular surgical approach that manages infection, soft tissue defects, stability, and alignment. Recent systematic reviews demonstrate high success rates with the following management options: Ilizarov bone transport, Masquelet (induced membrane) technique, cancellous bone grafting, and vascularized bone grafts.

Intrinsic universality in automata networks I: Families and simulations

An automata network (AN) is a finite graph where each node holds a state from a finite alphabet and is equipped with a local map defining the evolution of the state of the node depending on its neighbors. They are studied both from the dynamical and the computational complexity point of view. Inspired from well-established notions in the context of cellular automata, we develop a theory of intrinsic simulations and universality for families of automata networks. We establish many consequences of intrinsic universality in terms of complexity of orbits (periods of attractors, transients, etc) as well as hardness of the standard well-studied decision problems for automata networks (short/long term prediction, reachability, etc). In the way, we prove orthogonality results for these problems: the hardness of a single one does not imply hardness of the others, while intrinsic universality implies hardness of all of them. We also compare our notions of universality to intrinsic universality of cellular automata. This paper is the first of a series of three: in the second one, we develop a proof technique to establish intrinsic universality based on an operation of glueing; in the third one we study the effect of update schedules on intrinsic universality for concrete symmetric families of automata networks.

SEPs infringement and competition law defence in German case law

Within the debate on the role of competition law in standard essential patents (SEPs) litigation, German case law has taken centre stage because of the number of decisions handed down and its traditional patent-owner-friendly approach. As the framework handed down by the European Court of Justice (CJEU) in Huawei is usually interpreted as being opposed to the approach adopted by the German Federal Court in the Orange Book Standard decision, it is worth investigating how German courts have tackled the competition law defence in the post-Huawei scenario. The article maintains that the Federal Court’s rulings in Einwand have aligned German case law with the Huawei framework and that the attention devoted in German case law to the implementer’s obligations is consistent with the principles set out by the CJEU. Nonetheless, recent lower courts’ decisions confirm that the case-by-case detection of the licensee’s willingness is still a challenging issue.