Individual Expertise Research Articles

Do inhaled or oral glucocorticoids more effectively control feline asthma?

PICO question
 In cats with chronic bronchospasm and airway hypersensitivity (asthma) do oral glucocorticoids or inhaled glucocorticoids more effectively control the clinical signs?
 
 Clinical bottom line
 Category of research question
 Treatment
 The number and type of study designs reviewed
 Three prospective randomised clinical trials were appraised. Two of the studies followed a crossover design and had a control group, whilst the third study described an interrupted time series
 Strength of evidence
 Weak
 Outcomes reported
 The available studies deemed a reduction in eosinophilia on bronchoalveolar lavage fluid analysis, and a reduction in airway resistance as markers of treatment efficacy
 Conclusion
 There is weak evidence to suggest equal treatment efficacy of oral and inhaled glucocorticoid therapy for management of feline asthma. Higher powered studies would be required before a definitive recommendation can be made
 
 How to apply this evidence in practice
 The application of evidence into practice should take into account multiple factors, not limited to: individual clinical expertise, patient’s circumstances and owners’ values, country, location or clinic where you work, the individual case in front of you, the availability of therapies and resources.
 Knowledge Summaries are a resource to help reinforce or inform decision making. They do not override the responsibility or judgement of the practitioner to do what is best for the animal in their care.

Social Computing: A Model of Technology Acceptance to Maintain Micro Enterprise Business During Covid-19

MSMEs are entrepreneurship growth channels whose existence is recognized as the backbone of national economic development. However, not all MSMEs use technological developments in their business processes. Weak individual skills and expertise as a result of weak mastery of Science and Technology (IPTEK) in a general sense and technological mastery of related products and services in a specific sense. This has an impact on the acceptance of MSME actors for the existence of technology for business continuity, therefore this study aims to see how social computing can affect the technology acceptance model by micro-entrepreneurs in maintaining business. This research is a qualitative research with a phenomenological design. The object of this research is the five people of Padang City micro-entrepreneurs, selected purposively with snowball sampling . Data was collected by means of in-depth interviews and naturalistic observations. This study uses data triangulation to test the validity of the data. The results of this study found that technological efficiency can improve business performance. Besides, the use of technology is able to make the effectiveness of business continuity. The use of technology is also user-friendly when used by users. This study provides implications regarding social computing that occurs between business actors and technology adoption that affects business actors in accepting technology for business resilience.

Does the use of supraglottic device in rabbits cause less injury than other airway management devices?

PICO question
 In rabbits, undergoing general anaesthesia, does the placement of a v-gel® device result in less airway trauma compared to the use of other airway management devices?
 
 Clinical bottom line
 Category of research question
 Treatment
 The number and type of study designs reviewed
 Three papers were critically appraised, two blinded randomised experimental trial studies and one randomised crossover experimental trial study
 Strength of evidence
 Weak
 Outcomes reported
 One blinded randomised trial study demonstrated that the trauma to the upper airways of rabbits during anaesthesia is not significantly different between the use of v-gel® and an endotracheal tube. The other blinded randomised trial study demonstrated that the trauma to the upper airway caused by endotracheal intubation is significantly more than that caused by v-gel® placement. The randomised crossover trial study demonstrated that v-gel® placement causes more significant compression to the larynx compared to a laryngeal mask or face mask
 Conclusion
 The current evidence suggests that use of the v-gel® in rabbits causes less trauma / injury to the airways compared to placement of an endotracheal tube but not compared to the use of a laryngeal or face mask. However, based on the low number and quality of published studies, this evidence is weak, and better-quality studies are required to support the routine use of v-gel® over other airway devices in rabbits. While v-gel® may be a safer alternative for securing airways in rabbits compared to endotracheal intubation, knowing the injuries this device can cause to the upper airways is useful for managing rabbits during post anaesthesia recovery
 
 How to apply this evidence in practice
 The application of evidence into practice should take into account multiple factors, not limited to: individual clinical expertise, patient’s circumstances and owners’ values, country, location or clinic where you work, the individual case in front of you, the availability of therapies and resources.
 Knowledge Summaries are a resource to help reinforce or inform decision making. They do not override the responsibility or judgement of the practitioner to do what is best for the animal in their care.

Methodology of establishing communications with corporate consumers in the construction materials market

Modern world dictates its own conditions, and every entrepreneur must master the art of sales strategy, not only master it but also win the battle for profits and market share. Today, strategy is a long-term action plan aimed at achieving an enterprise’s global goals. Prior to forming a detailed by-element sales strategy, special attention must be paid to personnel policy because a large part of the success depends on a manager`s individual professional qualities, expertise, communication skills, knowledge, and practice in implementing sales techniques, etc. The authors explore the influence of various channels on the organization's sales volume and suggest basic tasks to form a “direct” selling strategy. The significance of a manager’s individual qualities as a mandatory prerequisite for the creation of a successful sales strategy has been established. Special attention is paid to the most effective sources of gathering information about construction objects of various types, which, in turn, allows constant monitoring of the sequence of works, acquisition of proper paperwork from the developer, determining constructional features of a building, and figure out construction technologies used on the object. Such an approach allows for reducing significantly the time spent on the preliminary stage of the sales – search, collection, and structuring information about the potential clients. An analysis of the received data on the implemented strategy was carried out, including all the mentioned components, which allowed, firstly, to cover the entire ground without losing the opportunity of the “touch” with every potential client and, secondly, to be fully prepared for the meeting with the client, which will be a solid foundation for sales techniques. On the basis of the received data, the effectiveness of the considered strategy and the sales department as a whole is evaluated. As a result, the most effective algorithm for materials sales in the construction market is suggested.

Принципи та моделі експертно-аналітичної методології підтримки формування адаптивних організаційних рішень за умов глибокої невизначеності

The paper depicts and analyzes Expert-Analytical Methodology named EAM DMDU to support Proactive Anti-crisis Decisions within Organizational Systems under deep uncertainty. Complex tools are proposed for Decisions Domain Knowledge analytical operation. The Benefit is no essential resource demands while keeping the basic principles to deal with deep uncertainty (uncertainties and inconsisten- cies eliciting; Decision vulnerabilities searching instead prediction; threats resilience priority over effectiveness). EAM DMDU enables Deliberative multi-staged Process for Adaptive Decision forming aimed at expected future conflict situation solving. The Process stages are: Problem situation Analysis, Impact on Problem Situation Goal Proposition, Goal proposals Assessment; Efforts for Goal achieving Proposals; Effort Proposals Assessment; reference Proposal option Selection and Decision adaptations accordingly to Decision Frame changes Recommendation. Knowledge operation is enabled with the procedures such as: formal analysis, individual expert assessment, Decision elements deliberative forming. EAM DMDU common information space of is based on Domain Ontology and ensures equal participants’ awareness, expert judgments with their arguments constructive representation and knowledge reuse. Expert-analytical Selection of Proposals uses their Perspectivity Model. It is a sub-goals hierarchy to achieve the goal being formed over previous Process stages. Hierarchy knot is represented with ontologically formalized definition for State of the Art corresponding sub-goal achievement. Leaf node depicts State of the Art with explicit expert Estimates of Certainty factor (from the Stanford model) being provided concerning its implementation through Decision element Proposal being assessed. The Estimate’s arguments are elements of information space used by expert. Under incomplete certainty of element expert provides its boundary values and State of the Art estimates both pessimistic and optimistic. Perspectivity Model contains also conditions for goal achievement violation being caused with environmental threats. Procedures for Estimates formal integration up to Model provide extreme estimates of Proposals Perspectivity and Robustness regarding current uncertainty. Under unsatisfactory properties of integrated Estimates their deliberative adjustment is carried out using Uncertainty Map and arguments provided. The final reference Decision contains selected Goal-Means option and guides to adapt it when decision frame changes. Further research is carried out for EAM DMDU instrumental tools development and its usage for defense resource management.

Expertise-Structure and Risk-Appetite-Integrated Two-Tiered Collective Opinion Generation Framework for Large-Scale Group Decision Making

The generation of collective preference assessments occupies a critical position in deriving accurate and reliable alternative rankings in the context of large-scale group decision making (LSGDM). In general, the collective opinion generation framework entails the following three phases, which are clustering analysis, weighting clusters, and preference aggregation. However, the clustering of experts has been frequently based on preference similarities among them without taking into account individual opinions in which knowledge elicitation plays a crucial role. The traditional collective opinion generation framework suffering from this drawback may result in unreliable decision outcomes. To this end, we propose an expertise-structure and risk-appetite-integrated two-tiered collective opinion generation framework to address this concern. The first tier of the two-tiered collective opinion generation framework divides the entire expert group into several subgroups based on individual expertise structures, which are extracted from hesitant fuzzy linguistic term set (HFLTS)-based preference assessments, and it then weighs the resulting clusters in accordance with the overall expertise levels. The second-tier clusters the first-tier subgroups conditioned on the indicator of individual assessment similarities and gathers the generated subgroup preference constructs in the use of the risk appetite-oriented power average operator. In addition, the notion of proportional HFLTSs was introduced to manifest collective evaluations in second-tier subgroups to eliminate information loss and distortion. The effectiveness and flexibility of the proposed collective opinion generation algorithm are eventually illustrated by a case study and a comparison analysis.

AI Based Learning Model Management Framework for Private Cloud Computing

<p>Artificial Intelligence (AI) systems are computational simulations that are “educated” using knowledge and individual expert participation to replicate a decision that a professional would make provided the same data. A model tries to simulate a specific decision loop that several scientists would take if they had access to all kinds of knowledge. To convey a model, you make a model asset in AI Platform Prediction, make a variant of that model and, at that point, interface the model form to the model record put away in Cloud Storage. AI and DB information sharing are essential for cutting-edge processing for DBMS innovation. The inspirations promoting their incorporation advances incorporate the requirement for admittance to a lot of data that is shared information handling, effective administration of data as information, and astute preparation of information. Notwithstanding these inspirations, the plan for a smart information base interface (IDI) was likewise spurred by the craving to save the considerable speculation spoke to by most existing data sets. A few general ways to deal with the connectivity of AI and databases and different improvements in the area of clever information bases were already examined and announced in this paper.</p> <p> </p>

Nutrition support teams: Institution, evolution, and innovation.

The historical institution, evolution, and innovations of nutrition support teams (NSTs) over the past six decades are presented. Focused aspects of the transition to transdisciplinary and patient-centered care, NST membership, leadership, and the future of NSTs are further discussed. NSTs were instituted to address the need for the safe implementation and management of parenteral nutrition, developed in the late 1960s, which requires the expertise of individuals working collaboratively in a multidisciplinary fashion. In 1976, the American Society for Parenteral and Enteral Nutrition (ASPEN) was established using the multidisciplinary model. In 1983, the United States established the inpatient prospective payment system with associated diagnosis-related groupings, which altered the provision of nutrition support in hospitals with funded NSTs. The number of funded NSTs has waxed and waned since; yet hospitals and healthcare have adapted, as additional education and experience grew, primarily through ASPEN's efforts. Nutrition support was not administered in some instances by the "core of four" (physician, nurse, dietitian, pharmacist). The functions may be carried out by a member of the core of four not associated with the parent discipline, in accordance with licensure/privileging. This cross-functioning has evolved into the adaptation of the concept of transdisciplinarity, emphasizing function over form, supported and enhanced by "top-of-license" practice. In some institutions, nutrition support has been incorporated into other healthcare teams. Future innovations will assist NSTs in providing the right nutrition support for the right patient in the right way at the right time, recognizing that nutrition care is a human right.

Sacrococcygeal teratomas: a case-based review from the perspective of individual multidisciplinary experts.

Sacrococcygeal teratoma (SCT) is a rare congenital tumor originating from a variant of extragonadal germ cell neoplasm. Herein, we present three cases of neonatal SCTs undergoing surgical resection and reconstruction. We also review the literature to discuss the importance of proper perinatal management and timely surgical intervention depending on the tumor type and maturity to prevent malignant transformation and recurrence and ensure functional outcomes. Three cases of SCT were retrospectively analyzed. All infants underwent complete surgical resection of the SCT and coccygectomy during their neonatal/infancy period, followed by pelvic floor and buttock reconstruction while minimizing buttock contour deformity and undesirable skin scar. Two of the cases were histopathologically diagnosed as predominantly cystic mature teratomas of Altman types I and IV, and the third was a mature teratoma of Altman type II. There were no complications or tumor recurrence during the average follow-up period of 5.3years. This case-based review highlights the role of multidisciplinary team approaches, including prenatal monitoring, oncologic resection, and adequate reconstruction according to the type of tumor and anomaly. Optimal perinatal evaluation allows promising oncologic and functional outcomes in terms of timely intervention to eliminate tumor recurrence and malignant transformation. Complete oncologic surgical resection of SCTs should also include proper functional preservation strategies, such as the reconstruction of the pelvic floor, cosmetic buttock contouring, and preservation of bladder and bowel sphincter function.

Does industry audit risk similarity affect auditor expertise? Evidence from China

ABSTRACT This study examines whether auditors develop expertise in audit risk homogenous industries. Using critical audit matters (CAMs) disclosed in audit reports of Chinese listed companies, we construct a homogeneity measure that captures the similarity of audit risks in a client industry. We find that individual partner expertise occurs in industries with high levels of CAM similarity. We also find that industry expert partners provide fee discounts in more homogenous industries, whereas audit quality is unchanged. These results suggest that auditors benefit from achieving economies of scale rather than providing quality-differentiated audits, by specializing in industries with similar audit risks.

Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise**

AbstractPhysico‐chemical continuum battery models are typically parameterized by manual fits, relying on the individual expertise of researchers. In this article, we introduce a computer algorithm that directly utilizes the experience of battery researchers to extract information from experimental data reproducibly. We extend Bayesian Optimization (BOLFI) with Expectation Propagation (EP) to create a black‐box optimizer suited for modular continuum battery models. Standard approaches compare the experimental data in its raw entirety to the model simulations. By dividing the data into physics‐based features, our data‐driven approach uses orders of magnitude less simulations. For validation, we process full‐cell GITT measurements to characterize the diffusivities of both electrodes non‐destructively. Our algorithm enables experimentators and theoreticians to investigate, verify, and record their insights. We intend this algorithm to be a tool for the accessible evaluation of experimental databases.

Does clicker training reduce stress in shelter cats?

PICO question
 In domestic cats in a shelter setting, does clicker training decrease proxy measures of a stressed emotional state (behavioural or physiological) compared to no clicker training?
 
 Clinical bottom line
 Category of research question
 Treatment
 The number and type of study designs reviewed
 Two papers were critically reviewed. One was a prospective quasi-randomised clinical trial, the other was a quasi-experimental before-and-after study with each cat being its own control
 Strength of evidence
 Weak
 Outcomes reported
 Both papers reported a decrease in behavioural stress indicators in shelter cats following a clicker training programme. Not all of these decreases were statistically significant and there are large issues with confounding factors in both papers
 Conclusion
 Preliminary evidence suggests that clicker training can be implemented as one form of enrichment to reduce stress in shelter cats alongside other means. Further evidence is required to demonstrate superiority to other forms of enrichment to reduce stress in shelter cats, especially given the practical limitations of implementing such a programme in most shelters
 
 How to apply this evidence in practice
 The application of evidence into practice should take into account multiple factors, not limited to: individual clinical expertise, patient’s circumstances and owners’ values, country, location or clinic where you work, the individual case in front of you, the availability of therapies and resources.
 Knowledge Summaries are a resource to help reinforce or inform decision making. They do not override the responsibility or judgement of the practitioner to do what is best for the animal in their care.

Improving pediatric idiopathic intracranial hypertension care: a retrospective cohort study

To describe the clinical course and prognosis of pediatric idiopathic intracranial hypertension (IIH) and examine the preferred management setting.IIH is characterized by increased intracranial pressure and is often associated with headaches and visual complaints. IIH is a preventable cause of vision loss in children. Hence, a rapid diagnosis followed by prompt treatment and follow-up is essential. However, standardization of the management of IIH in the pediatric population is not well established. Computerized medical charts of all 82 pediatric (< 18 years) patients diagnosed with IIH between 2007 and 2018 in the metropolitan area of Jerusalem were reviewed. Comparison was made between children followed in a multidisciplinary clinic in tertiary centers and those followed elsewhere. Detailed demographic and clinical data, as well as data regarding the follow-up setting and clinical course of the disease, were collected and analyzed. Recurrent IIH-related hospital returns were selected as a measurable marker for the uncontrolled disease. Recurrent IIH-related hospital return rate was significantly lower and occurred later among children followed by multidisciplinary teams compared to individual experts. Follow-up in multidisciplinary clinics improve the quality of life, and financial burden and may prevent permanent visual impairment in children with IIH.

Gastric dilatation volvulus in dogs: utility of lactate as a predictor of survival

PICO question&#x0D; In dogs presenting with gastric dilatation volvulus, is an admission or pre-operative lactate level a reliable predictor of survival to discharge?&#x0D; &#x0D; Clinical bottom line&#x0D; Category of research question&#x0D; Prognosis&#x0D; The number and type of study designs reviewed&#x0D; 15 studies (12 retrospective and 3 prospective) were critically appraised&#x0D; Strength of evidence&#x0D; Moderate&#x0D; Outcomes reported&#x0D; At a population level, lower blood lactate concentration, or lactate concentration that decreases following fluid resuscitation, are associated with a better prognosis.&#x0D; At a population level, higher blood lactate concentration, or lactate concentration that fails to decrease following fluid resuscitation, is associated with a worse prognosis. However, the lower sensitivity across studies means that a high lactate, or one that does not decrease following fluid therapy, should be interpreted more cautiously than a low lactate; i.e., low lactate predicts survival better than high lactate predicts non-survival.&#x0D; In all studies, there was a significant overlap in individual blood lactate concentration between survivors and non-survivors&#x0D; Conclusion&#x0D; Blood lactate level should only be used to help guide broad, cautiously worded conversations with owners as described above. It should not be used to give a prognosis for individual patients. The overlap between survivors and non-survivors, and the high overall survival rate, means that exploratory laparotomy should be advised irrespective of the blood lactate level&#x0D; &#x0D; How to apply this evidence in practice&#x0D; The application of evidence into practice should take into account multiple factors, not limited to: individual clinical expertise, patient’s circumstances and owners’ values, country, location or clinic where you work, the individual case in front of you, the availability of therapies and resources.&#x0D; Knowledge Summaries are a resource to help reinforce or inform decision making. They do not override the responsibility or judgement of the practitioner to do what is best for the animal in their care.

Visual preference for abstract curvature and for interior spaces: Beyond undergraduate student samples.

Smoothly curved objects elicit feelings of pleasantness, and tend to be preferred over angular objects. Furthermore, individual differences (i.e., art expertise, openness to experience, holistic thinking), and the complexity of the stimuli are known to moderate the effect. We extended the study of individual differences to two theoretically-relevant groups. Study 1 compared liking for curvature in individuals with autism and a matched neurotypical control group (for age, gender and IQ). Because preference for curvature depends on both sensory (visuospatial) and affective input, for which individuals with autism exhibit anomalies, we hypothesized a difference in preference for curved stimuli between the two groups. Study 2 examined preference for curvature in a group of quasi-expert students of design. Because working architects and designers tend to regard curved interior spaces as beautiful, we hypothesized to replicate this effect within quasi-experts as well, thereby extending the effect across levels of expertise. Using an identical methodology across both studies, we administered abstract stimuli consisting of irregular polygons (angular vs. curved) and patterns of coloured lines (angular vs. curved), as well as concrete stimuli consisting of images of interior spaces. Preference for curvature was confirmed with abstract stimuli in all three groups. For interior design, the curvature effect diminished in magnitude, and this was especially evident in individuals with autism. Interestingly, quasi-experts preferred rectilinear over curvilinear interiors. We discuss the results in relation to the impact of individual differences and expertise on preference for curvature, and their implication for design studies in ecologically valid settings.

Defining plant ecological specialists and generalists: Building a framework for identification and classification.

Specialization is a widespread but highly ambiguous and context-dependent ecological concept. This quality makes comparisons across related studies difficult and makes associated terms such as "specialist" and "generalist" scientifically obscure. Here, we present a metric-based framework to quantify specialization in 141 Quercus species using functional traits, biogeography, and species interactions. Rankings of specialization based on five metrics were used to answer questions about how specialization is used colloquially (i.e., individual species assessment by experts) and influenced by phylogenetics (Ancestral Character State Reconstruction, Automatic Shift Detection), biogeography (patterns of clustering by region and with climate), and species threat level (IUCN Red List). Metric-based ranking can be representative of specialization in a consistent and practical manner, correlating with IUCN Red List data, and the mean scores of individual expert assessments. Specialization is shown to be highly correlated with precipitation seasonality and only moderately influenced by evolutionary history. Data-deficient species were more likely to be highly specialized, and higher specialization was positively correlated with greater IUCN threat level. Frameworks for characterizing specialization and generalization can be done using metric ranking and can turn concepts that are often unclear into a definitive system. Metric-based rankings of specialization can also be used to reveal interesting insights about a clade's evolutionary history and geographic distribution when paired with the related phylogenetic and geographic data. Metric-based rankings can be applied to other systems and be a valuable tool for identifying species at risk and in need of conservation.

STRUNZ, Ulrich G. (2001): The Impact of Individual Expertise and Public Information on Group Decision-Making

STRUNZ, Ulrich G. (2001): The Impact of Individual Expertise and Public Information on Group Decision-Making

Group Decision Making-Based Fusion for Human Activity Recognition in Body Sensor Networks.

Ensemble learning systems (ELS) have been widely utilized for human activity recognition (HAR) with multiple homogeneous or heterogeneous sensors. However, traditional ensemble approaches for HAR cannot always work well due to insufficient accuracy and diversity of base classifiers, the absence of ensemble pruning, as well as the inefficiency of the fusion strategy. To overcome these problems, this paper proposes a novel selective ensemble approach with group decision-making (GDM) for decision-level fusion in HAR. As a result, the fusion process in the ELS is transformed into an abstract process that includes individual experts (base classifiers) making decisions with the GDM fusion strategy. Firstly, a set of diverse local base classifiers are constructed through the corresponding mechanism of the base classifier and the sensor. Secondly, the pruning methods and the number of selected base classifiers for the fusion phase are determined by considering the diversity among base classifiers and the accuracy of candidate classifiers. Two ensemble pruning methods are utilized: mixed diversity measure and complementarity measure. Thirdly, component decision information from the selected base classifiers is combined by using the GDM fusion strategy and the recognition results of the HAR approach can be obtained. Experimental results on two public activity recognition datasets (The OPPORTUNITY dataset; Daily and Sports Activity Dataset (DSAD)) suggest that the proposed GDM-based approach outperforms the well-known fusion techniques and other state-of-the-art approaches in the literature.

Facts and arguments checking: investigating the occurrence of scientific arguments on Twitter

A method for studying use of scientific sources in arguments on Twitter is demonstrated. Data were collected from the Twitter API v. 2.0 using Focalevents, searching for tweets with links to DOIs, and then collecting conversations around these tweets. Analysis. Three conversations on different topics were analysed searching for argumentative behaviour, use of scientific sources, their reliability, consistency and adequacy in relation to the argument and the target audience. Both quantitative and qualitative content analysis based on argumentation theory were applied. The method allowed us to identify scientific publications used argumentatively by a multiple audience in the context of Twitter conversations. The publications were used to build scientific arguments, mainly, but not exclusively, from individual and collegial expert opinion. Scientific findings were often misinterpreted and used improperly to the benefit of the argument. Through the use of argumentation theory to study conversations in a structured way, the paper demonstrates how to approach the usage of scientific publications in arguments. Scientific publications were used to build scientific arguments from different types of expert opinion, for giving proofs for claims and ounter-arguments, and inconsistent or biased arguments from individual expert opinion.

Interobserver Agreement among Multiple Generalists is Comparable to that of Recognized Experts: Prospective Acceptability Benchmarks from the C3RO Crowdsourced Initiative

<h3>Purpose/Objective(s)</h3> Contouring Collaborative for Consensus in Radiation Oncology (C3RO) is a public crowdsourced challenge engaging radiation oncologists across various expertise levels in cloud-based image-segmentation. A challenge in artificial intelligence (AI) development is the relative paucity of multi-expert observer datasets sufficiently large to train deep learning; consequently, we sought to characterize whether aggregate segmentations generated from large numbers of generalists could meet or exceed expert interobserver agreement, the current "gold standard." <h3>Materials/Methods</h3> Participants who contoured at least one region of interest (ROI) for the C3RO breast or sarcoma challenge were identified as generalist or recognized expert. Cohort-specific ROIs were combined into single simultaneous truth and performance level estimation (STAPLE) consensus segmentations. STAPLE_generalist ROIs were evaluated against STAPLE_expert contours using Dice Similarity Coefficient (DSC). The expert interobserver DSC (IODSC_expert) (i.e., pairwise median DSC across experts) was calculated as a performance acceptability threshold between STAPLE_generalist and STAPLE_expert. To determine the number of generalists required to match the IODSC_expert for each ROI, a single STAPLE_bootstrap consensus contour was generated for a 10-fold random-bootstrap using a variable number of generalists (between 2-25) and then compared to the IODSC_expert. <h3>Results</h3> The breast challenge yielded contours from 124 generalists and 8 experts. The DSC for STAPLE_generalist versus STAPLE_expert were higher than their respective expert IODSC_expert for all ROIs, including the axilla (STAPLE_generalist/IODSC_expert, 0.86/0.68), chest wall (0.91/0.67), heart (0.97/0.9), supraclavicular nodes (0.77/0.57), internal mammary nodes (0.66/0.46), left brachial plexus (0.46/0.2), and left anterior descending artery (0.62/0.32). The sarcoma case had contours from 61 generalists and 4 experts. The DSC between STAPLE_generalist and STAPLE_expert were higher than their respective IODSC_expert for GTV (0.97/0.94) and CTV (0.76/0.69), but not genitalia (0.60/0.66). The theoretical minimum number of generalist segmentation needed to cross the IODSC_expert acceptability threshold ranged between 2-4 for breast and 2-5 for sarcoma ROIs. <h3>Conclusion</h3> Multi-generalist-generated consensus ROIs met or exceeded expert-derived acceptability thresholds. Analyses suggest that 5+ generalists could potentially generate consensus ROIs with DSC performance approximating an individual expert, suggesting multi-generalist segmentations as a feasible AI input. Future research will explore whether these observations are site-specific and/or generalizable to more granular surface metrics.