Iterative Consensus Research Articles

A qualitative exploration of active ingredients and mechanisms of action of an online singing programme with mothers experiencing postnatal depression during the COVID-19 pandemic: SHAPER-PNDO study

BackgroundSocial distancing restrictions and the suspension of in-person treatment and support contributed to an increase in postnatal depression during the coronavirus disease 2019 (COVID-19) pandemic. Creative health interventions can help to alleviate anxiety and depression, with studies showing that singing is particularly effective for supporting the mental health of new mothers. We adapted an in-person group singing programme (Breathe Melodies for Mums (M4M)) to online delivery during the COVID-19 pandemic to support the mental health of new mothers, and, in a feasibility study, found improvements in postnatal depression (PND) symptoms at 6-month follow up. The current qualitative study aimed to explore how and why M4M-online impacted the mental health of those taking part.MethodsWe took a theory-based approach using the INgredients iN ArTs in hEealth (INNATE) Framework of ‘active ingredients’ and the Multi-level Leisure Mechanisms Framework of ‘mechanisms of action’ to identify and categorise intervention components and change mechanisms. Iterative consensus building between three researchers was complemented by qualitative semi-structured online interviews with 24 women experiencing PND symptoms who took part in M4M-online. Data were analysed inductively using reflexive thematic analysis.ResultsConsistency was found between the online and in-person interventions in active ingredients relating to project design, content, programme management and the composition of the group. Key differences were in the social and contextual ingredients. Psychological, social and behavioural mechanisms for improved mental health and wellbeing included: (1) Increased self-confidence as a mother, (2) Increased positive emotional responses, (3) A supported exploration of self-dentity, (4) Reduced loneliness and isolation, (5) Increased social bonding and connections with family and (6) Enhanced sense of time through new routines.ConclusionsParticipating in online group singing can support new mothers experiencing PND by triggering psychological, social and behavioural responses that lead to improved mental health. Key programme features are identified which can be used to design future online creative health interventions or tailor in-person activities for remote delivery to support populations who may face practical and social barriers to attending in-person.

Radial basis function neural network based data-driven iterative learning consensus tracking for unknown multi-agent systems

This paper provides a novel data-driven-distributed-consensus control protocol for unknown nonlinear non-affine discrete-time multi-agent systems (MAS) with repetitive properties. The leader’s commands are directed to the followers in the topological graph. The dynamic linearization technology (DLT) is used to build the distributed iterative learning (IL) controller along the iteration axis. In the iterative process, the control gain is automatically adjusted by updating the weight matrix of the high-order radial basis function neural network (RBFNN, HORBFNN). In global control, the higher order parameter (HOP) Newton method is used to achieve global convergence and stability of the control process. All the above processes do not require the understanding of dynamical equations or physical models for each agent, and only use local communication information of multi-agent to achieve consistent tracking of MAS leaders and followers. Based on the strong connection, the convergence performance, stability and boundedness properties of the proposed control protocol in the fixed topology as well as in the iterative topology are validated by a rigorous theoretical analysis. Simulation experiments are conducted to verify the effectiveness of the control protocol.

Robust Data-Driven Compensation Iterative Learning Consensus Tracking Control for MIMO Multiagent Systems With Random Packet Dropouts

Robust Data-Driven Compensation Iterative Learning Consensus Tracking Control for MIMO Multiagent Systems With Random Packet Dropouts

The many facets of perceived bladder health in women: Absence of symptoms and presence of healthy behaviors across the life course

Purpose:Qualitative studies exploring bladder health are rare compared to research focusing on lower urinary tract symptoms (LUTS). Our aim was to explore adolescent and adult women’s perceptions about what constitutes a healthy bladder. Methods:We conducted a 7-site focus group study of bladder health with adolescent and adult women across six age categories (11–14 to 65+ years). Transcripts were analyzed using transdisciplinary, directed content analysis and an iterative interpretive consensus building approach to identify key constructs, focusing on “healthy bladder” and “unhealthy bladder” codes. Results:Forty-four focus groups with 360 participants were completed. Three thematic categories were identified: (1) concept of a healthy bladder (conceptual abstraction); (2) experience of a healthy bladder (subjective experience); and (3) lifestyle and life course considerations (bladder health in context). Participants struggled to define the unfamiliar concept of healthy bladder and relied on contextualized experiences and behaviors for characterizing bladder health. They described the concept of a healthy bladder as something that you did not need to think about but did require attention to healthy habits to maintain. Other features of a healthy bladder discussed include having bladder control despite urgency and environmental constraints on voiding, normal voiding frequency, and qualities of urine and urination. Participants struggled with lack of information about healthy bladder habits and noted absence of routine bladder health screening. Discussion:Findings identify need to promote public education and information sharing in health care visits about bladder health. Further they can inform bladder health promotion and clinical research on LUTS prevention.

Toward a responsible future: recommendations for AI-enabled clinical decision support.

Integrating artificial intelligence (AI) in healthcare settings has the potential to benefit clinical decision-making. Addressing challenges such as ensuring trustworthiness, mitigating bias, and maintaining safety is paramount. The lack of established methodologies for pre- and post-deployment evaluation of AI tools regarding crucial attributes such as transparency, performance monitoring, and adverse event reporting makes this situation challenging. This paper aims to make practical suggestions for creating methods, rules, and guidelines to ensure that the development, testing, supervision, and use of AI in clinical decision support (CDS) systems are done well and safely for patients. In May 2023, the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association co-sponsored a working group on AI in healthcare. In August 2023, there were 4 webinars on AI topics and a 2-day workshop in September 2023 for consensus-building. The event included over 200 industry stakeholders, including clinicians, software developers, academics, ethicists, attorneys, government policy experts, scientists, and patients. The goal was to identify challenges associated with the trusted use of AI-enabled CDS in medical practice. Key issues were identified, and solutions were proposed through qualitative analysis and a 4-month iterative consensus process. Our work culminated in several key recommendations: (1) building safe and trustworthy systems; (2) developing validation, verification, and certification processes for AI-CDS systems; (3) providing a means of safety monitoring and reporting at the national level; and (4) ensuring that appropriate documentation and end-user training are provided. AI-enabled Clinical Decision Support (AI-CDS) systems promise to revolutionize healthcare decision-making, necessitating a comprehensive framework for their development, implementation, and regulation that emphasizes trustworthiness, transparency, and safety. This framework encompasses various aspects including model training, explainability, validation, certification, monitoring, and continuous evaluation, while also addressing challenges such as data privacy, fairness, and the need for regulatory oversight to ensure responsible integration of AI into clinical workflow. Achieving responsible AI-CDS systems requires a collective effort from many healthcare stakeholders. This involves implementing robust safety, monitoring, and transparency measures while fostering innovation. Future steps include testing and piloting proposed trust mechanisms, such as safety reporting protocols, and establishing best practice guidelines.

A Proposed Framework for Rigor and Transparency in Dysphagia Research: Prologue.

Scientific transparency and rigor are essential for the successful translation of knowledge in clinical research. However, the field of oropharyngeal dysphagia research lacks guidelines for methodological design and reporting, hindering accurate interpretation and replication. This article introduces the Framework for RigOr aNd Transparency In REseaRch on Swallowing (FRONTIERS), a new critical appraisal tool intended to support optimal study design and results reporting. The purpose of introducing FRONTIERS at this early phase is to invite pilot use of the tool and open commentary. FRONTIERS was developed by collaborating researchers and trainees from six international dysphagia research labs. Eight domains were identified, related to study design, swallowing assessment methods, and oropharyngeal dysphagia intervention reporting. Small groups generated questions capturing rigor and transparency for each domain, based on examples from the literature. An iterative consensus process informed the refinement and organization of primary and subquestions, culminating in the current initial version of FRONTIERS. FRONTIERS is a novel tool, intended for use by oropharyngeal dysphagia researchers and research consumers across disciplines. A web application enables provisional use of the tool, and an accompanying survey solicits feedback regarding the framework. FRONTIERS seeks to foster rigor and transparency in the design and reporting of oropharyngeal dysphagia research. We encourage provisional use and invite user feedback. A future expert consensus review is planned to incorporate feedback. By promoting scientific rigor and transparency, we hope that FRONTIERS will support evidence-based practice and contribute to improved health outcomes for individuals with oropharyngeal dysphagia.

Iterative learning resilient consensus of uncertain nonlinear multi-agent systems vulnerable to false data injection attacks

This work investigates the iterative learning resilient consensus of uncertain nonlinear high-order multi-agent systems (MASs) against false data injection (FDI) attacks. First, in order to reduce the impact of FDI attacks on the nonlinear MASs, a novel coordinate transformation technique is proposed, which is composed of the states after being attacked, and the Nussbaum gain technique is adopted to address the problem of unknown attack gains resulting from FDI attacks. Then, by employing compromised state variables, a fuzzy adaptive iterative learning resilient control method is presented based on the composite energy function, where unknown nonlinear functions are handled using fuzzy logic systems. The presented approach can guarantee that the outputs of all followers precisely track the leader in a limited time interval while ensuring that all closed-loop signals are bounded. Finally, a numerical simulation is provided to demonstrate the efficacy of the designed control strategy.

AI assistance in aesthetic medicine-A consensus on objective medical standards.

Aesthetic medicine has traditionally relied on clinical scales for the objective assessment of baseline appearance and treatment outcomes. However, the scales focus on limited aesthetic areas mostly and subjective interpretation inherent in these scales can lead to variability, which undermines standardization efforts. The consensus meeting aimed to establish guidelines for AI application in aesthetic medicine. In February 2024, the AI Consensus Group, comprising international experts in various specialties, convened to deliberate on AI in aesthetic medicine. The methodology included a pre-consensus survey and an iterative consensus process during the meeting. AI's implementation in Aesthetic Medicine has achieved full consensus for enhancing patient assessment and consultation, ensuring standardized care. AI's role in preventing overcorrection is recognized, alongside the need for validated objective facial assessments. Emphasis is placed on comprehensive facial aesthetic evaluations using indices such as the Facial Aesthetic Index (FAI), Facial Youth Index (FYI), and Skin Quality Index (SQI). These evaluations are to be gender-specific and exclude makeup-covered skin at baseline. Age and gender, as well as patients' ancestral roots, are to be considered integral to the AI assessment process, underlining the move towards personalized, precise treatments. The consensus meeting established that AI will significantly improve aesthetic medicine by standardizing patient assessments and consultations, with a strong endorsement for preventing overcorrection and advocating for validated, objective facial assessments. Utilizing indices such as the FAI, FYI, and SQI allows for gender-specific, age adjusted evaluations and insists on a makeup-free baseline for accuracy.

Distributed iterative learning consensus tracking for singular partial differential multi-agent systems under fixed and iteration-varying topologies

This paper investigates the consensus tracking problems of singular partial differential multi-agent systems (SPD-MASs) with initial state errors under fixed and iteration-varying network topologies, where only certain follower agents have access the trajectory defined by virtual leaders. To solve this problem, a distributed iterative learning consensus protocol with initial state learning is proposed, ensuring accurate tracking of the leader's trajectory by all follower agents with initial state errors. The convergence conditions for consensus tracking error are derived through mathematical analysis between adjacent agents. Theoretical analysis shows that, under convergence conditions, the consensus error between any two agents can converge to zero along the iterative axis. Finally, numerical simulations show validate the effectiveness of the proposed distributed control protocol with initial state learning compared to traditional protocols without state learning.

International Classification System for Ocular Complications of Anti-VEGF Agents in Clinical Trials

Complications associated with intravitreal anti-VEGF therapies are reported inconsistently in the literature, thus limiting an accurate evaluation and comparison of safety between studies. This study aimed to develop a standardized classification system for anti-VEGF ocular complications using the Delphi consensus process. Systematic review and Delphi consensus process. Twenty-five international retinal specialists participated in the Delphi consensus survey. A systematic literature search was conducted to identify complications of intravitreal anti-VEGF agent administration based on randomized controlled trials (RCTs) of anti-VEGF therapy. A comprehensive list of complications was derived from these studies, and this list was subjected to iterative Delphi consensus surveys involving international retinal specialists who voted on inclusion, exclusion, rephrasing, and addition of complications. Furthermore, surveys determined specifiers for the selected complications. This iterative process helped to refine the final classification system. The proportion of retinal specialists who choose to include or exclude complications associated with anti-VEGF administration. After screening 18 229 articles, 130 complications were categorized from 145 included RCTs. Participant consensus via the Delphi method resulted in the inclusion of 91 complications (70%) after 3 rounds. After incorporating further modifications made based on participant suggestions, such as rewording certain phrases and combining similar terms, 24 redundant complications were removed, leaving a total of 67 complications (52%) in the final list. A total of 14 complications (11%) met exclusion thresholds and were eliminated by participants across both rounds. All other remaining complications not meeting inclusion or exclusion thresholds also were excluded from the final classification system after the Delphi process terminated. In addition, 47 of 75 proposed complication specifiers (63%) were included based on participant agreement. Using the Delphi consensus process, a comprehensive, standardized classification system consisting of 67 ocular complications and 47 unique specifiers was established for intravitreal anti-VEGF agents in clinical trials. The adoption of this system in future trials could improve consistency and quality of adverse event reporting, potentially facilitating more accurate risk-benefit analyses. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Iterative approximate Byzantine consensus in arbitrary directed graphs

Iterative approximate Byzantine consensus in arbitrary directed graphs

Diagnosis of methamphetamine-induced psychotic disorder: Findings of an expert consensus panel.

We define criteria for methamphetamine-induced psychotic disorder (MIPD) to aid in accurate and reliable diagnosis. An expert panel was recruited and engaged in an iterative consensus process. A literature search supported this work. The a priori level for consensus was considered ≥80% of voting panellists. The final expert panel included 22 physicians from different backgrounds and practice environments. The panel produced two consensus diagnoses: (1) acute MIPD and (2) persisting MIPD, which is further separated into subacute and chronic timeframes. Although certain characteristics differentiate methamphetamine-induced psychosis shortly after use, identification of persisting MIPD depends largely on a history of symptom onset. All respondents voted in the final round, and both criteria were fully endorsed by 91% (20/22) of respondents. Panellists further recommended next steps in validation and research on this topic. These diagnostic criteria aid clinicians in differentiating methamphetamine-induced psychotic symptoms from psychosis because of other psychiatric disorders and can guide future studies. Future research might examine these criteria's prognostic significance, interrater reliability and acceptability including among persons in recovery. This work is a necessary and vital step in advancing the science of methamphetamine addiction treatment.

SPIN-PM: a consensus framework to evaluate the presence of spin in studies on prediction models

ObjectivesTo develop a framework to identify and evaluate spin practices and its facilitators in studies on clinical prediction model regardless of the modeling technique. Study Design and SettingWe followed a three-phase consensus process: (1) premeeting literature review to generate items to be included; (2) a series of structured meetings to provide comments discussed and exchanged viewpoints on items to be included with a panel of experienced researchers; and (3) postmeeting review on final list of items and examples to be included. Through this iterative consensus process, a framework was derived after all panel's researchers agreed. ResultsThis consensus process involved a panel of eight researchers and resulted in SPIN-Prediction Models which consists of two categories of spin (misleading interpretation and misleading transportability), and within these categories, two forms of spin (spin practices and facilitators of spin). We provide criteria and examples. ConclusionWe proposed this guidance aiming to facilitate not only the accurate reporting but also an accurate interpretation and extrapolation of clinical prediction models which will likely improve the reporting quality of subsequent research, as well as reduce research waste.

Diagnosis and initial management of children presenting with premature loss of primary teeth associated with a systemic condition: A scoping review and development of clinical aid.

Premature loss of primary teeth (PLPT) can be a rare presentation of systemic medical conditions. Premature loss of primary teeth may present a diagnostic dilemma to paediatric dentists. To identify systemic conditions associated with PLPT and develop a clinical aid. OVID Medline, Embase and Web of Science were searched up to March 2023. Citation searching of review publications occurred. Exclusion occurred for conference abstracts, absence of PLPT and absence of English-language full text. Seven hundred and ninety-one publications were identified via databases and 476 by citation searching of review articles. Removal of 390 duplicates occurred. Following the exclusion of 466 records on abstract review, 411 publications were sought for retrieval, of which 142 met inclusion criteria. Thirty-one systemic conditions were identified. For 19 conditions, only one publication was identified. The majority of publications, 91% (n = 129), were case reports or series. Most publications, 44% (n = 62), were related to hypophosphatasia, and 25% (n = 35) were related to Papillon-Lefèvre. Diagnostic features were synthesised, and a clinical aid was produced by an iterative consensus approach. A diverse range of systemic diseases are associated with PLPT. Evidence quality, however, is low, with most diseases having a low number of supporting cases. This clinical aid supports paediatric dentists in differential diagnosis and onward referral.

The strategy of consensus and consistency improving considering bounded confidence for group interval-valued intuitionistic multiplicative best-worst method

To significantly and reasonably enhance the individual consistency and group consensus in group best-worst method (GBWM) under interval-valued intuitionistic multiplicative environment, this study proposes a strategy of consensus and consistency improving considering bounded confidence. Firstly, based on the consistency definition of interval-valued intuitionistic multiplicative preference relations (IVIMPRs), the consistency definition for interval-valued intuitionistic multiplicative reference comparison relations (IVIMRCRs) is proposed. Subsequently, a two-stage consistency improving optimization model is constructed. Then, an interactively iterative consistency improving algorithm considering bounded confidence is designed. For GBWM with IVIMRCRs, the concept of consensus measure is first defined to assess the consensus level among individual IVIMRCRs, and then a two-stage consensus improving optimization model considering bounded confidence is constructed. Additionally, based on the above theories and models, an interactively iterative consensus improving algorithm considering bounded confidence and consistency management is designed for GBWM with IVIMRCRs. Finally, the practicality and effectiveness of the proposed GBWM are verified through an actual application case and simulation analyses. Meanwhile, the advantages of this method are explained through comparative analyses with the existing BWMs and group decision making (GDM) with IVIMPRs.

A core outcome set for acute necrotizing pancreatitis: An Eastern Association for the Surgery of Trauma modified Delphi method consensus study.

The management of acute necrotizing pancreatitis (ANP) has changed dramatically over the past 20 years including the use of less invasive techniques, the timing of interventions, nutritional management, and antimicrobial management. This study sought to create a core outcome set (COS) to help shape future research by establishing a minimal set of essential outcomes that will facilitate future comparisons and pooling of data while minimizing reporting bias. A modified Delphi process was performed through involvement of ANP content experts. Each expert proposed a list of outcomes for consideration, and the panel anonymously scored the outcomes on a 9-point Likert scale. Core outcome consensus defined a priori as >70% of scores receiving 7 to 9 points and <15% of scores receiving 1 to 3 points. Feedback and aggregate data were shared between rounds with interclass correlation trends used to determine the end of the study. A total of 19 experts agreed to participate in the study with 16 (84%) participating through study completion. Forty-three outcomes were initially considered with 16 reaching consensuses after four rounds of the modified Delphi process. The final COS included outcomes related to mortality, organ failure, complications, interventions/management, and social factors. Through an iterative consensus process, content experts agreed on a COS for the management of ANP. This will help shape future research to generate data suitable for pooling and other statistical analyses that may guide clinical practice. Therapeutic/Care Management; Level V.

Human papillomavirus negative high grade cervical lesions and cancers: Suggested guidance for HPV testing quality assurance

BackgroundSome high-grade cervical lesions and cervical cancers (HSIL+) test negative for human papillomavirus (HPV). The HPV-negative fraction varies between 0.03 % and 15 % between different laboratories. Monitoring and extended re-analysis of HPV-negative HSIL+ could thus be helpful to monitor performance of HPV testing services. We aimed to a) provide a real-life example of a quality assurance (QA) program based on re-analysis of HPV-negative HSIL+ and b) develop international guidance for QA of HPV testing services based on standardized identification of apparently HPV-negative HSIL+ and extended re-analysis, either by the primary laboratory or by a national HPV reference laboratory (NRL). MethodsThere were 116 initially HPV-negative cervical specimens (31 histopathology specimens and 85 liquid-based cytology samples) sent to the Swedish HPV Reference Laboratory for re-testing. Based on the results, an international QA guidance was developed through an iterative consensus process. ResultStandard PCR testing detected HPV in 55.2 % (64/116) of initially “HPV-negative” samples. Whole genome sequencing of PCR-negative samples identified HPV in an additional 7 samples (overall 61.2 % HPV positivity). Reasons for failure to detect HPV in an HSIL+ lesion are listed and guidance to identify cases for extended re-testing, including which information should be included when referring samples to an NRL are presented. ConclusionMonitoring the proportion of and reasons for failure to detect HPV in HSIL+ will help support high performance and quality improvement of HPV testing services. We encourage implementation of QA strategies based on re-analysis of “HPV negative” HSIL+ samples.

First episode psychosis caregiver perspectives on motivational interviewing for loved ones training: A qualitative study.

Past research has found that family involvement in psychosis treatment leads to better patient outcomes. Thus, caregiver communication skills training can be a viable approach to reducing caregiver stress and increasing self-efficacy and communication. The purpose of this qualitative study was to describe family caregivers' perceptions of changes in themselves and their family member with psychosis following their participation in Motivational Interviewing in Loved Ones (MILO), a brief four to five-hour psychoeducational intervention for caregivers. Sixty-three participants in the MILO trials provided written qualitative responses to the prompt, "Since learning the ideas and techniques in this course, what is the most significant change you noticed in yourself, your family, or your relationships?" Responses were collected immediately following MILO participation and 12 weeks later. Qualitative themes were identified through an iterative consensus process. Most participants reported positive changes in multiple domains of their lives. Major themes included: (1) Changes in Self, (2) Changes in Relationships, (3) Changes in Mindset, (4) Use of MILO Skills, and (5) Challenges. Qualitative results support and add context to the previously reported quantitative results from this study. MILO is a promising family intervention that positively influenced family environment and communication in pilot trials. Adaptations of MILO for families outside of a highly educated North American context should be considered.

Observer-Based Event-Triggered Iterative Learning Consensus for Locally Lipschitz Nonlinear MASs

This paper aims to realize the robust tracking for nonidentical locally Lipschitz nonlinear multiagent systems (MASs) with unmeasurable states, for which an observer-based distributed event-triggered iterative learning control (ILC) framework is proposed. With this framework, distributed state observers provide the indispensable state information for agents to learn to complete the task. An initial state learning strategy is implemented to relax consistent initial conditions, which can improve the learning accuracy. An event-triggering mechanism is designed to reduce the occupation of communication and computing resources during the iterative learning process of MASs. With locally Lipschitz nonlinearities and iteration-varying uncertainties caused by nonrepetitive initial states, the double-dynamics analysis (DDA) method is adopted to illustrate the convergence of the ILC process. By setting appropriate learning gains and constructing a quasi-globally Lipschitz condition via the DDA method, the robust convergence can be achieved for MASs in the presence of switching topologies. A numerical simulation is used to show the validity of the proposed framework.

Abstract WP52: Evaluation of Post Stroke Telehealth Services in Texas (Vast-1)

Background: Shortly after AHA/ASA released an updated policy on stroke care systems, COVID-19 disrupted these systems. Lockdowns and social distancing were considered critical to mitigate COVID-19 transmission but altered public perceptions about stroke medical care. As a result, telehealth expanded rapidly across Texas. However, little is known how to optimally integrate telehealth services into the continuum of stroke care. Methods: To identify barriers and facilitators of post-acute stroke telehealth services among stroke survivors’ facilities of the Lone Star Stroke Consortium (LSSC) Research Consortium, we conducted semi-structured interviews with stroke providers and focus groups with patients. Audiotapes were transcribed, and files prepared for analysis. Using a grounded theory approach, we performed data analysis to allow theory to emerge from data. Two independent reviewers read transcripts, developed coding using an iterative consensus building, verified codes, developed concepts, and created categories. Constant comparison process found emerging themes within each category. Results: We enrolled 23 providers and 34 patients. Access, telemedicine encounter and technology were revealed as major categories of post-acute stroke telehealth among providers and patients. Facilitators of access included increased accessibility and elimination of barriers. Reliable internet connection and technological skills were barriers for both groups. Patients and providers identified virtual visits as good. However, full medical exam, including cognitive evaluation, was a barrier for patients and providers. Technology increased ability to communicate with patients and providers. Barriers included constrains of internet services, lack of documentation for virtual visits, and lack of telemedicine platform. Conclusion: Our study will contribute to future telehealth services integration into continuum of stroke care.