Iterative Validation Process Research Articles

Psychometric Validation of the New Misophonia Screening List-Child and Youth and AMISOS-Y (Dutch Child- and Parent-Report Versions) for Assessing Misophonia in Youth.

Misophonia is a recently identified disorder of decreased sound tolerance that often originates in childhood. Currently, there is a lack of validated questionnaires for screening and assessing misophonia severity in children/adolescents. This paper presents an iterative validation process of two innovative (parallel child-/parent-reported) questionnaires: the Misophonia Screening List-Child and Youth for screening, and the Amsterdam Misophonia Scale-Youth (AMISOS-Y) for assessing misophonia severity in youth. After instrument refinement, we performed ROC curve, reliability, and principal component analyses, and assessed concurrent, convergent and divergent validity, on a combined sample (aged 8-18; clinical sample N = 94 youth, 95 parents; control group screening N = 197 youth, 56 parents; control group AMISOS-Y N = 192 youth, 55 parents). Both questionnaires were unidimensional and displayed excellent psychometric properties (α = 0.95-0.96). Future replication studies are needed in community and clinical samples to contribute to a unified diagnostic framework. Trial registered 09/2021: NL-OMON20775.

Modelling Climate Change Impacts on Location Suitability for Cultivating Avocado and Blueberry in New Zealand

Regional suitability for growing avocados and blueberries may alter with climate change. Modelling can provide insights into potential climate change impacts, thereby informing industry and government policy decisions to ameliorate future risks and capitalise on future opportunities. We developed continuous/sliding-scale models that used soil, terrain and weather data to assess location suitability for cultivating avocado and blueberry, based on physiological and phenological considerations specific to each crop. Using geographical information system (GIS) data on soil, slope and weather, we mapped cultivation suitability for avocado and blueberry across New Zealand, and, for accuracy, “ground-truthed” these maps in an iterative process of expert validation and model recalibration. We modelled the incremental changes in location suitability that could occur through climate change using “future” GIS-based weather data from climate model simulations for different greenhouse gas (GHG) pathways that ranged from stringent GHG mitigation to unabated GHG emissions. Changes in maps over time showed where suitability would increase or decrease and to what extent. These results indicate where avocado and blueberry might replace other crops that become less suitable over time, and where avocado might displace blueberry. The approach and models can be applied to other countries or extended to other crops with similar growing requirements.

Creating Digital Content for Traditional Children's Games in North Sumatra to Preserve Culture and Enhance Sports Participation

As globalization progresses, local cultures, including traditional games, are increasingly at risk of erosion. There is a pressing need to develop strategies to preserve these cultural elements, introduce them to younger generations, and enhance active participation in sports. This study focuses on creating multimedia content based on traditional games from the North Sumatra region to achieve these goals. The research utilized the Research and Development (RD) model by Borg and Gall, which includes ten systematic steps. Participants included 40 children aged 6-12 years and 40 adolescents aged 13-15 years from North Sumatra, divided into individual and small group trials. The setting comprised elementary and junior high schools in Deliserdang district and Medan city. The development process involved initial design, expert validation, iterative revisions, and field trials to test and refine the multimedia content. The developed multimedia content, featuring animated video media of traditional game activities, was validated by experts and received high feasibility ratings. Initial validation stages yielded a feasibility percentage of 73.80%, improving to 95.20% after revisions. Material validation assessments also showed significant improvement, with feasibility increasing from 75.55% to 92.22%. Field trials indicated that the content was well-received by participants, effectively preserving regional culture and promoting active participation in sports. The study demonstrates the potential of multimedia content in preserving cultural heritage and encouraging sports participation among youth. The positive feedback from expert validators and participants underscores the content's educational and cultural value. However, further research with larger and more diverse samples is recommended to confirm these findings and explore long-term impacts. The multimedia content developed through this research effectively preserves traditional games from North Sumatra and promotes active participation in sports among children and adolescents. The iterative validation and testing process ensured high feasibility and user engagement, making it a valuable tool for cultural and educational initiatives.

Adolescent sexual and reproductive health literacy needs: a sub-national level assessment in Ghana.

This study assessed young adolescents' access and literacy challenges to sexual and reproductive health information and knowledge gaps in the Effutu Municipality in the Central Region of Ghana. We used a narrative design and a focus group discussion method to glean data from 52 in-school adolescents, aged 11-15. Focus group discussions were conducted using a discussion guide and data were processed using QDA Miner (version 6.0). We analyzed data thematically using an iterative process of data validation, coding and recording. The participants had poor knowledge of the concepts of sexual and reproductive health and its essential domains. Abstinence was the predominant sexual and reproductive health goal of the participants. Yet, many males admitted to having sexual partners. There was difficult linguistic and physical access to structured and safe information. Personal hygiene, signs of sexual maturation and abstinence were the predominant contents available to young adolescents. A revision of adolescent health policies and strategies to embrace actions for improving unrestricted access to easy-to-read educational materials and adolescent health literacy is essential. Creating adolescent corners and introducing peer education in schools within Effutu and other municipalities in the Central Region will be useful.

Early Detection of Breast Cancer using Deep Learning in Mammograms

Breast cancer still poses a serious threat to world health, needing creative approaches to early identification in order to improve patient outcomes. This study investigates the potential of deep learning methods to improve the precision and effectiveness of mammography interpretation for the identification of breast cancer. In this paper proposed, a convolutional neural network (CNN) architecture, ResNet50, is created and trained on a sizable data set of annotated mammograms. The CNN is made to automatically identify and extract pertinent elements, such as microcalcifications, masses, and architectural distortions,that may be symptomatic of possible cancers. The model develops the ability to distinguish between benign and malignant instances through an iterative process of training and validation, finally displaying a high level of discriminatory accuracy. The paper findings show that the deep learning model outperforms conventional mammography interpretations in terms of sensitivity and specificity for detecting breast cancer. Furthermore, the model's potential for use in actual clinical settings is highlighted by its generalizability across a range of patient demographics and imaging technologies.This study represents a big step in improving radiologists' capacity for breast cancer early detection. Our deep learning-based architecture has promise for improving the screening procedure and potentially decreasing the difficulties brought on by radiologist shortages by lowering false positives, improving accuracy, and offering quick analysis. By utilising cutting-edge technology to enable prompt and efficient detection, this study contributes to continuing efforts by the international healthcare community to improve breast cancer outcomes.

Co-creation of the Global Patient Experience Data Navigator: a multi-stakeholder initiative to ensure the patient voice is represented in health decision-making

BackgroundPutting patients’ needs and priorities at the forefront of healthcare initiatives and medical product development is critical to achieve outcomes that matter most to patients. This relies on the integration of early, meaningful patient engagement (PE) to learn what is important to patients, and collection of representative patient experience data (PXD). The increased number of PE/PXD efforts across global regulatory, health technology assessment, and healthcare systems is an important step forward to deliver improved health outcomes for patients. However, these initiatives are fragmented and lack integration, which is necessary to maximize efforts and reduce burden on patients. To overcome these challenges, the Global Patient Experience Data Navigator has been co-created by Patient Focused Medicines Development to provide practical resources that can facilitate and optimize PXD generation, collection, analysis, and dissemination for patient benefit and aims to be applicable across all therapeutic areas for all stakeholders.MethodsCo-creation of the Navigator took place through an iterative process of validation and formalization driven by a diverse, multi-stakeholder working group with individuals who have varying knowledge/experience in PE/PXD.ResultsA series of workshops took place to conduct a gap analysis, develop a taxonomy model, and integrate existing frameworks. The collective insights led to the development of the Navigator consisting of four specific tools in the form of downloadable templates, which can be used to: (1) prioritize outcomes that matter most to patients and their caregivers; (2) select appropriate measurement methods for these outcomes; (3) identify when and why PXD is used throughout the product development cycle for each stakeholder; (4) identify when and why PXD is used throughout the healthcare process for each stakeholder. A public consultation was carried out to collect user feedback before the Navigator was made publicly available in December 2022.ConclusionTo our knowledge, the Global Patient Experience Data Navigator is the only publicly available toolkit developed with a multi-stakeholder and disease-agnostic approach providing taxonomically grouped resources to optimize the collection and collation of PXD for patient benefit. Future work will aim to further engage patients by adding a PE dimension to the Navigator.

Validation of Bayesian design for broadband microslit panel absorbers using causal inference.

This paper discusses experimental validations of multilayer microslit panels (MSPs) designed via Bayesian inference to obtain both high sound absorption and wide bandwidth simultaneously. Microslit perforation in thin panels is similar to microperforated panels [Xiang, Fackler, Hou, and Schmitt (2022). J. Acoust. Soc. Am. 151(5), 3094-3103]. MSP absorbers in single-layer configurations are functioning in a limited frequency range. By stacking the MSPs in multiple layered structures, absorbing performance may be widened in frequency ranges while retaining high absorption coefficients. Besides design challenges of multiple MSPs in layered structures to fulfill a practical requirement and minimize fabrication complexity, this paper further discusses challenges in experimental validations when experimental results undesirably deviate from the initial Bayesian design. Causation analysis is applied to the validation efforts where a causal model-based inference effectively provides causal reasoning of fabrication inaccuracies. Along with the causal inference, a causal reasoning conducted in this work can guide corrections due to fabrication inaccuracies during the iterative validation process.

A Redesign of the ACGME Resident/Fellow Survey Through Expert Reviews and Cognitive Interviews.

Since 2003, the Accreditation Council for Graduate Medical Education (ACGME) has surveyed residents and fellows in its accredited programs. The Resident/Fellow Survey is a screening and compliance tool that programs can also use for continuous quality improvement. However, stakeholders have reported potential problems with the survey's overall quality and credibility. To redesign the 2006 Resident/Fellow Survey using expert reviews and cognitive interviews. In 2018-2019, the ACGME redesigned the Resident/Fellow Survey using an iterative validation process: expert reviews (evidence based on content) and cognitive interviews (evidence based on response processes). Expert reviews were conducted by a survey design firm and an ACGME Task Force; cognitive interviews were conducted with a diverse set of 27 residents and fellows. Expert reviews resulted in 21 new survey items to address the ACGME's updated accreditation requirements; these reviews also led to improvements that align the survey items with evidence-informed standards. After these changes were made, cognitive interviews resulted in additional revisions to sensitive items, item order, and response option order, labels, and conceptual spacing. In all, cognitive interviews led to 11 item deletions and other improvements designed to reduce response error. Expert reviews and cognitive interviews were used to redesign the Resident/Fellow Survey. The content of the redesigned survey aligns with the updated accreditation requirements and items are written in accordance with evidence-informed standards. Additionally, cognitive interviews resulted in revisions to the survey that seem to improve respondents' comprehension and willingness to respond to individual survey items.

Revolutionizing Fertilization Strategies with Machine Learning-Driven Nutrient Prediction

In the pursuit of sustainable agriculture, the effective management of fertilization strategies stands as a critical imperative. This abstract explores the transformative potential encapsulated within "Revolutionizing Fertilization Strategies with Machine Learning-Driven Nutrient Prediction." Traditional agricultural practices often grapple with imprecise fertilization, leading to inefficiencies, overuse of resources, and environmental ramifications. This study introduces an innovative approach that integrates advanced Machine Learning (ML) techniques with agronomic insights to accurately predict plant nutrient requirements. By harnessing comprehensive datasets encompassing soil properties, crop categorizations, and historical growth data, an intricate ML model is formulated. Employing sophisticated algorithms such as Random Forest, XG Boost Classifier the model uncovers intricate interdependencies shaping nutrient absorption. Through an iterative process of training, validation, and optimization, the model attains the capability to anticipate nuanced nutrient demands across diverse growth stages and crop typologies. By supplying real-time, data-informed intelligence on nutrient requirements, farmers are empowered to tailor fertilization approaches with precision, curbing resource wastage and diminishing nutrient runoff. Additionally, this ML-Centered methodology aligns seamlessly with the ambitions of sustainable agriculture, channelling efforts toward resource efficiency and environmental stewardship.

Why Definitional Clarity Matters: Implications for the Operationalization of Emotional Well-Being.

The National Institutes of Health (NIH) is increasingly prioritizing research on health-promoting processes. Park et al. (this issue) respond to a call made by NIH to advance the study of emotional well-being (EWB) and to increase understanding of the fundamental constituents of EWB across the lifespan and among diverse subgroups. They propose a definition of EWB that provides an organizing framework for research on 'psychological aspects of well-being' and health. We commend this important first step and urge consideration of three important issues related to operationalization - the process by which an abstract concept is transformed into variables that can be measured - in future research on EWB. We expect that an iterative process of construct refinement and empirical validation will advance the study of EWB, producing scientific discoveries that can be leveraged to enhance health across the lifespan.

UNDERSTANDING CONSTRUCTION SITE SAFETY HAZARDS THROUGH OPEN DATA: TEXT MINING APPROACH

Construction is an industry well known for its very high rate of injuries and accidents around the world. Even though many researchers are engaged in analysing the risks of this industry using various techniques, construction accidents still require much attention in safety science. According to existing literature, it has been found that hazards related to workers, technology, natural factors, surrounding activities and organisational factors are primary causes of accidents. Yet, there has been limited research aimed to ascertain the extent of these hazards based on the actual reported accidents. Therefore, the study presented in this paper was conducted with the purpose of devising an approach to extract sources of hazards from publicly available injury reports by using Text Mining (TM) and Natural Language Processing (NLP) techniques. This paper presents a methodology to develop a rule-based extraction tool by providing full details of lexicon building, devising extraction rules and the iterative process of testing and validation. In addition, the developed rule-based classifier was compared with, and found to outperform, the existing statistical classifiers such as Support Vector Machine (SVM), Kernel SVM, K-nearest neighbours, Naïve Bayesian classifier and Random Forest classifier. The finding using the developed tool identified the worker factor as the highest contributor to construction site accidents followed by technological factor, surrounding activities, organisational factor, and natural factor (1%). The developed tool could be used to quickly extract the sources of hazards by converting largely available unstructured digital accident data to structured attributes allowing better data-driven safety management.

An immersive performative architectural design tool with daylighting simulations: a building information modeling-based approach

PurposeIn performative architectural design, daylighting is a crucial design consideration; however, the evaluation of daylighting in the design process can be challenging. Immersive environments (IEs) can create a dynamic, multi-sensory, first-person view in computer-generated environments, and can improve designers' visual perception and awareness during performative design processes. This research addresses the need for interactive and integrated design tools for IEs toward better-performing architectural solutions in terms of daylighting illumination. In this context, building information modeling and performance simulations are identified as critical technologies to be integrated into performative architectural design.Design/methodology/approachThis research adopts a design science research (DSR) methodology involving an iterative process of development, validation and improvement of a novel and immersive tool, HoloArch, that supports design development during daylighting-informed design processes. HoloArch was implemented in a game engine during a spiral software development process. HoloArch allows users to interact with, visualize, modify and explore architectural models. The evaluation is performed in two workshops and a user study. A hybrid approach that combines qualitative and quantitative data collection was adopted for evaluation. Qualitative data analyses involve interviews, while quantitative data analyses involve both daylighting simulations and questionnaires (e.g. technology acceptance model (TAM), presence and system usability scale (SUS)).FindingsAccording to the questionnaire results, HoloArch had 92/100 for SUS, a mean value of 120.4 for presence questionnaire (PQ) and 9.4/10 for TAM. According to the simulation results, all participants improved the given building's daylighting performance using HoloArch. The interviews also indicated that HoloArch is an effective design tool in terms of augmented perception, continuous design processes, performative daylighting design and model interaction. However, challenges still remain regarding the complete integration of tools and simultaneous simulation visualization. The study concludes that IEs hold promising potentials where performative design actions at conceptual, spatial and architectural domains can take place interactively and simultaneously with immediate feedback.Originality/valueThe research integrates building information modeling (BIM), performative daylighting simulations and IEs in an interactive environment for the identification of potentials and limitations in performative architectural design. Different from existing immersive tools for architecture, HoloArch offers a continuous bidirectional workflow between BIM tools and IEs.

Measuring teacher motivation: The missing link between professional development and practice

This study explores an important but less explored outcome of professional development, teachers’ motivation to integrate professional development into practice. We developed a scale that measures teachers’ motivation to implement professional development in their classrooms. Three samples of teachers (N = 1388) were used in an iterative process of scale development and validation. Results suggest this scale is a reliable tool to measure teachers’ expectations for successful implementation, value for implementing, and the perceived costs of implementing.

Efficient User Guidance for Validating Participatory Sensing Data

Participatory sensing has become a new data collection paradigm that leverages the wisdom of the crowd for big data applications without spending cost to buy dedicated sensors. It collects data from human sensors by using their own devices such as cell phone accelerometers, cameras, and GPS devices. This benefit comes with a drawback: human sensors are arbitrary and inherently uncertain due to the lack of quality guarantee. Moreover, participatory sensing data are time series that exhibit not only highly irregular dependencies on time but also high variance between sensors. To overcome these limitations, we formulate the problem of validating uncertain time series collected by participatory sensors. In this article, we approach the problem by an iterative validation process on top of a probabilistic time series model. First, we generate a series of probability distributions from raw data by tailoring a state-of-the-art dynamical model, namely <u>G</u>eneralised <u>A</u>uto <u>R</u>egressive <u>C</u>onditional <u>H</u>eteroskedasticity (GARCH), for our joint time series setting. Second, we design a feedback process that consists of an adaptive aggregation model to unify the joint probabilistic time series and an efficient user guidance model to validate aggregated data with minimal effort. Through extensive experimentation, we demonstrate the efficiency and effectiveness of our approach on both real data and synthetic data. Highlights from our experiences include the fast running time of a probabilistic model, the robustness of an aggregation model to outliers, and the significant effort saving of a guidance model.

ITEMAS ontology for healthcare technology innovation

BackgroundThe Platform for Innovation in Medical and Health Technologies (ITEMAS) is a network of 66 healthcare centres focused on fostering innovation in medical and health technologies as an essential tool for increasing the sustainability of the Spanish healthcare system. The present research is focused on defining a formal representation that details the most relevant concepts associated with the creation and adoption of innovative medical technology in the Spanish healthcare system.MethodsThe methodology applied is based on the methontology process, including peer-review identification and selection of concepts from the ITEMAS innovation indicators and innovation management system standards. This stage was followed by an iterative validation process. Concepts were then conceptualised, formalised and implemented in an ontology.ResultsThe ontology defined describes how relationships between employees, organisations, projects and ideas can be applied to generate results that are transferrable to the market, general public and scientific forums. Overall, we identified 136 concepts, 138 object properties and 30 properties in a five-level hierarchy. The ontology was tested and validated as an appropriate framework for calculating the ITEMAS innovation indicators.ConclusionsThe consensus concepts were expressed in the form of an ontology to be used as a single communication format between the members of the ITEMAS network. Healthcare centres can compare their innovation results and obtain a better understanding of their innovation context based on the reasoning techniques of artificial intelligence. As a result, they can benefit from advanced analytical capabilities to define the most appropriate innovation policies for each centre based on the common experience of the large number of healthcare centres involved. The results can be used to create a map of agents and knowledge to show capabilities, projects and services provided by each of the participating centres. The ontology could also be applied as an instrument to match needs with existing projects and capabilities from the community of organisations working in healthcare technology innovation.

Development of an iterative validation process for a 30-day hospital readmission prediction index.

A study was conducted to determine if an iterative validation process could maintain or improve the discriminative and predictive capabilities of a 30-day hospital readmission prediction index over 2.5 years. Patient admissions were retrospectively identified using the electronic medical record. The receiver operating characteristic curve was used to assess model discrimination. Prediction index specificity, sensitivity, and positive and negative predictive values were also assessed. A rolling iterative validation process was developed in which patient admissions were divided into 3-month cohorts. Each cohort was analyzed individually and then included into the cumulative patient cohort and analyzed again. From 121,277 patient visits, an iterative validation approach maintained the discrimination (0.71 to 0.72), predictive validity, and overall accuracy (80.9% to 81.7%) of the 30-day readmission prediction index over 2.5 years. Index sensitivity and negative predictive value increased from baseline while specificity and positive predictive value remained largely unchanged. None of the assessed index parameters diminished or became less useful over the course of the study. An internal iterative validation process based on frequentist statistics maintained the discriminative ability and accuracy of a readmission index over 2.5 years despite numerous changes in the variables associated with readmission in the patient population.

Reduced Kinetic Models for Methane Flame Simulations

The present paper describes the development of two reduced kinetic schemes suitable for multidimensional turbulent flame simulations in high-temperature oxidation of methane. Formal reduction of the USC Mech II C1-C4 detailed kinetic model by using the directed relations graph mechanism results in a 31-species derivative scheme for lean to near-stoichiometric conditions. To deduce a still shorter, simpler, and less stiff kinetic model, further species elimination is based on combined sensitivity and chemical time scale information to arrive at a 22-species scheme. The kinetic rates of lumped reactions are here expressed as simple Arrhenius rates, avoiding nonlinear algebraic combinations of excluded elementary steps or species. The accuracy is maintained by tuning pre-exponential constants in the global Arrhenius rate expressions and computing a range of target data. A more compact, quasi-global 14-species scheme is subsequently formulated by modeling fuel decomposition to a methyl radical pool, followed by CH3 oxidation with O and OH toward CH2 and CO, and retaining a full CO/H2/O2 subset. The C2-chain with recombination of CH3 into C2H6 and production of C2H2 is also represented in both schemes. Equilibrium 0D and 1D propagating premixed flames and axisymmetric co-flowing lifted laminar jet flames are computed through an iterative validation process. Accompanying computations with the USC Mech II mechanism, as well as available experimental results, are exploited for optimization. The comparisons demonstrate that the derived schemes ensure satisfactory agreement with data over the investigated parameter space.

Codesigning a Measure of Person-Centred Coordinated Care to Capture the Experience of the Patient: The Development of the P3CEQ.

Background:Person-centred coordinated care (P3C) is a priority for stakeholders (ie, patients, carers, professionals, policy makers). As a part of the development of an evaluation framework for P3C, we set out to identify patient-reported experience measures (PREMs) suitable for routine measurement and feedback during the development of services.Methods:A rapid review of the literature was undertaken to identity existing PREMs suitable for the probing person-centred and/or coordinated care. Of 74 measures identified, 7 met our inclusion criteria. We critically examined these against core domains and subdomains of P3C. Measures were then presented to stakeholders in codesign workshops to explore acceptability, utility, and their strengths/weaknesses.Results:The Long-Term Condition 6 questionnaire was preferred for its short length, utility, and tone. However, it lacked key questions in each core domain, and in response to requests from our codesign group, new questions were added to cover consideration as a whole person, coordination, care plans, carer involvement, and a single coordinator. Cognitive interviews, on-going codesign, and mapping to core P3C domains resulted in the refinement of the questionnaire to 11 items with 1 trigger question. The 11-item modified version was renamed the P3C Experiences Questionnaire.Conclusions:Due to a dearth of brief measures available to capture people’s experience of P3C for routine practice, an existing measure was modified using an iterative process of adaption and validation through codesign workshops. Next steps include psychometric validation and modification for people with dementia and learning difficulties.

Development of a Hazard Classification Scheme for Substances Used in the Fraudulent Adulteration of Foods

Food fraud, the intentional misrepresentation of the true identity of a food product or ingredient for economic gain, is a threat to consumer confidence and public health and has received increased attention from both regulators and the food industry. Following updates to food safety certification standards and publication of new U.S. regulatory requirements, we undertook a project to (i) develop a scheme to classify food fraud-related adulterants based on their potential health hazard and (ii) apply this scheme to the adulterants in a database of 2,970 food fraud records. The classification scheme was developed by a panel of experts in food safety and toxicology from the food industry, academia, and the U.S. Food and Drug Administration. Categories and subcategories were created through an iterative process of proposal, review, and validation using a subset of substances known to be associated with the fraudulent adulteration of foods. Once developed, the scheme was applied to the adulterants in the database. The resulting scheme included three broad categories: 1, potentially hazardous adulterants; 2, adulterants that are unlikely to be hazardous; and 3, unclassifiable adulterants. Categories 1 and 2 consisted of seven subcategories intended to further define the range of hazard potential for adulterants. Application of the scheme to the 1,294 adulterants in the database resulted in 45% of adulterants classified in category 1 (potentially hazardous). Twenty-seven percent of the 1,294 adulterants had a history of causing consumer illness or death, were associated with safety-related regulatory action, or were classified as allergens. These results reinforce the importance of including a consideration of food fraud-related adulterants in food safety systems. This classification scheme supports food fraud mitigation efforts and hazard identification as required in the U.S. Food Safety Modernization Act Preventive Controls Rules.

Building and Crossing the Translational Bridge: 2016Alopecia Areata Research Summit Highlights.

Alopecia areata (AA) is a common autoimmune skin disease that results in the loss of hair on the scalp and elsewhere on the body and affects over 146 million people worldwide at some point in their lives. Founded in 1981, the National Alopecia Areata Foundation is a nonprofit organization that supports research to find a cure or acceptable treatment for AA, supports those with the disease, and educates the public about AA. The National Alopecia Areata Foundation conducts research summits every 2 years to review progress and create new directions in its funded and promoted research. The Foundation brings together scientists from all disciplines to get a broad and varied perspective. These AA research summits are part of the Foundation's main strategic initiative, the AA Treatment Development Program, to enhance the understanding of AA and accelerate progress toward a viable treatment.