Background Information Research Articles

Current and Ongoing Developments in Targeting Clostridioides difficile Infection and Recurrence

Clostridioides difficile is a Gram-positive, spore-forming anaerobic bacterial pathogen that causes severe gastrointestinal infection in humans. This review provides background information on C. difficile infection and the pathogenesis and toxigenicity of C. difficile. The risk factors, causes, and the problem of recurrence of disease and current therapeutic treatments are also discussed. Recent therapeutic developments are reviewed including small molecules that inhibit toxin formation, disrupt the cell membrane, inhibit the sporulation process, and activate the host immune system in cells. Other treatments discussed include faecal microbiota treatment, antibody-based immunotherapies, probiotics, vaccines, and violet-blue light disinfection.

Cross-modal collaborative feature representation via Transformer-based multimodal mixers for RGB-T crowd counting

RGB-T crowd counting aims to utilize the thermal source data to compensate for the insufficient RGB feature representation under low-illumination conditions. However, how to efficiently use complementary information from different modalities is still a challenging issue for the target task. Existing CNN-based RGB-T crowd counting methods focus on the cross-modal feature representation via the local fixed-receptive-field convolutional operations and the transformer-based works mainly utilize the mutual attention, failing to realize the global cross-modal feature representation. To this end, this paper designs a multimodal counting network based on the multimodal transformer mixer to realize cross-modal collaborative feature representation. Firstly, we design a Transformer-based multimodal mixer to fully fuse the features from both modalities. And then we utilize the Transformer-based network with the multi-head self-attention layer replaced by the average pooling layer to build the two-stream backbone to extract rich multi-scale feature information of the two modalities. Meanwhile, the features extracted from the mixers are used to enhance the intermediate features to achieve the preservation of crowd detail information and suppression of background information. Finally, we design a pyramid regression head to aggregate the multi-scale feature maps to regress the density maps. Extensive experiments, as well as ablation studies, are conducted on challenging RGB-T crowd counting benchmark datasets (RGBT-CC and DroneRGBT), and the achieved competitive experimental results demonstrate the effectiveness of the proposed method on cross-modal collaborative feature representation for target task.

A novel lameness detection method for dairy cows based on temporal gait and spatial post features

Lameness is one of the common diseases in dairy farms, which seriously affects the health and welfare of dairy cows. The existing lameness detection methods based on computer vision have poor ability to extract spatiotemporal features, which reduces the lameness detection accuracy in dairy cows. In this paper, a spatiotemporal energy network (SEN) is proposed to detect lameness in dairy cows. It compresses the cow walking video into history energy image (HEI) and gait energy image (GEI), and then accurately extracts the temporal gait and spatial pose features of dairy cows. First, the YOLOv8n algorithm is used to remove background information from the video and segment the cow’s body. Second, the body contour sequence of the dairy cow is stacked based on the time series, and then different body parts of dairy cows are stacked on the channel to form the HEI. The HEI preserves the movement trajectory of the dairy cows. The first lameness classification model is obtained by training the MobileNetv2 network with HEI dataset. Third, the body contour sequence of the dairy cow is cropped according to the center of mass and body proportion and then stacked into an image, which is named GEI. The second lameness classification model is obtained by training the MobileNetv2 network with GEI dataset. Finally, the prediction results of the two models are fused according to the weight ratio to detect lameness in dairy cows. To verify the lameness detection effect of this method, 264 samples were randomly selected from 330 samples as the training set and the remaining 66 samples as the test set. The proposed method obtained competitive lameness detection results with classification accuracy of 96.22 %, sensitivity of 96.35 % and specificity of 98.14 %. By combining HEI and GEI, the spatiotemporal features of the cow walking are extracted more comprehensively. This method provides guidance for the lameness detection of dairy cows and reference for the gait analysis of animals.

A cross-sectional questionnaire study: Impaired awareness of hypoglycaemia remains prevalent in adults with type 1 diabetes and is associated with the risk of severe hypoglycaemia.

Impaired awareness of hypoglycaemia (IAH) is a risk factor for severe hypoglycaemia (SH) in type 1 diabetes (T1D). Much of the IAH prevalence data comes from older studies where participants did not have the benefit of the latest insulins and technologies. This study surveyed the prevalence of IAH and SH in a tertiary adult clinic population and investigated the associated factors. Adults (≥18 years) attending a tertiary T1D clinic completed a questionnaire, including a Gold and Clarke score. Background information was collected from health records. 189 people (56.1% female) with T1D (median [IQR] disease duration 19.3 [11.5, 29.1] years and age of 41.0 [29.0, 52.0] years) participated. 17.5% had IAH and 16.0% reported ≥1 episode of SH in the previous 12 months. Those with IAH were more likely to report SH (37.5% versus 11.7%, p = 0.001) a greater number of SH episodes per person (median [IQR] 0 [0,2] versus 0 [0,0] P<0.001) and be female (72.7% versus 52.6%, p = 0.036). Socio-economic deprivation was associated with IAH (p = 0.032) and SH (p = 0.005). Use of technology was the same between IAH vs aware groups, however, participants reporting SH were more likely to use multiple daily injections (p = 0.026). Higher detectable C-peptide concentrations were associated with a reduced risk of SH (p = 0.04). Insulin pump and continuous glucose monitor use was comparable in IAH versus aware groups. Despite this, IAH remains a risk factor for SH and is prevalent in females and in older people. Socioeconomic deprivation was associated with IAH and SH, making this an important population to target for interventions.

Evaluating the Historical and Cultural Tourism Potential of Gwalior: A Comprehensive Analysis

This paper explores the tourism potential of Gwalior, a historical city with rich cultural heritage. By providing background information on the city, it aims to identify key factors shaping its tourism landscape. The research objectives include studying tourism potential, identifying challenges faced by locals and tourists, assessing existing infrastructure facilities, and delineating the scope for tourism development. Primary data was collected through a multiple-choice questionnaire, targeting both locals and tourists, to decode prevalent issues. Additionally, the quality of urban spaces was analysed across various parameters. This study contributes to understanding Gwalior's tourism prospects and provides insights for planning interventions for enhancing visitor experiences and fostering sustainable tourism growth in the city.

Triage Performance Across Large Language Models, ChatGPT, and Untrained Doctors in Emergency Medicine: Comparative Study.

Large language models (LLMs) have demonstrated impressive performances in various medical domains, prompting an exploration of their potential utility within the high-demand setting of emergency department (ED) triage. This study evaluated the triage proficiency of different LLMs and ChatGPT, an LLM-based chatbot, compared to professionally trained ED staff and untrained personnel. We further explored whether LLM responses could guide untrained staff in effective triage. This study aimed to assess the efficacy of LLMs and the associated product ChatGPT in ED triage compared to personnel of varying training status and to investigate if the models' responses can enhance the triage proficiency of untrained personnel. A total of 124 anonymized case vignettes were triaged by untrained doctors; different versions of currently available LLMs; ChatGPT; and professionally trained raters, who subsequently agreed on a consensus set according to the Manchester Triage System (MTS). The prototypical vignettes were adapted from cases at a tertiary ED in Germany. The main outcome was the level of agreement between raters' MTS level assignments, measured via quadratic-weighted Cohen κ. The extent of over- and undertriage was also determined. Notably, instances of ChatGPT were prompted using zero-shot approaches without extensive background information on the MTS. The tested LLMs included raw GPT-4, Llama 3 70B, Gemini 1.5, and Mixtral 8x7b. GPT-4-based ChatGPT and untrained doctors showed substantial agreement with the consensus triage of professional raters (κ=mean 0.67, SD 0.037 and κ=mean 0.68, SD 0.056, respectively), significantly exceeding the performance of GPT-3.5-based ChatGPT (κ=mean 0.54, SD 0.024; P<.001). When untrained doctors used this LLM for second-opinion triage, there was a slight but statistically insignificant performance increase (κ=mean 0.70, SD 0.047; P=.97). Other tested LLMs performed similar to or worse than GPT-4-based ChatGPT or showed odd triaging behavior with the used parameters. LLMs and ChatGPT models tended toward overtriage, whereas untrained doctors undertriaged. While LLMs and the LLM-based product ChatGPT do not yet match professionally trained raters, their best models' triage proficiency equals that of untrained ED doctors. In its current form, LLMs or ChatGPT thus did not demonstrate gold-standard performance in ED triage and, in the setting of this study, failed to significantly improve untrained doctors' triage when used as decision support. Notable performance enhancements in newer LLM versions over older ones hint at future improvements with further technological development and specific training.

Entity-centric multi-domain transformer for improving generalization in fake news detection

Fake news has become a significant concern in recent times, particularly during the COVID-19 pandemic, as spreading false information can pose significant public health risks. Although many models have been suggested to detect fake news, they are often limited in their ability to extend to new emerging domains since they are designed for a single domain. Previous studies on multidomain fake news detection have focused on developing models that can perform well on multiple domains, but they often lack the ability to generalize to new unseen domains, which limits their effectiveness. To overcome this limitation, in this paper, we propose the Entity-centric Multi-domain Transformer (EMT) model. EMT uses entities in the news as key components in learning domain-invariant and domain-specific news representations, which addresses the challenges of domain shift and incomplete domain labeling in multidomain fake news detection. It incorporates entity background information from external knowledge sources to enhance fine-grained news domain representation. EMT consists of a Domain-Invariant (DI) encoder, a Domain-Specific (DS) encoder, and a Cross-Domain Transformer (CT) that facilitates investigation of domain relationships and knowledge interaction with input news, enabling effective generalization. We evaluate the EMT's performance in multi-domain fake news detection across three settings: supervised multi-domain, zero-shot setting on new unseen domain, and limited samples from new domain. EMT demonstrates greater stability than state-of-the-art models when dealing with domain changes and varying training data. Specifically, in the zero-shot setting on new unseen domains, EMT achieves a good F1 score of approximately 72 %. The results highlight the effectiveness of EMT's entity-centric approach and its potential for real-world applications, as it demonstrates the ability to adapt to various training settings and outperform existing models in handling limited label data and adapting to previously unseen domains.

Major Psychological Factors Affecting Short Distance Athlete’s Performance in Hadiya Zone Athletics Projects

This study is explored the major psychological factors like personality and anxiety affects short distance athlete’s performance of Hadiya Zone athletics projects. The researcher was used both probability and non-probability sampling technique. Used stratified sampling techniques for athletes and available / census for all coaches and sport experts. The population of the study consisted of athletes (n=83), coaches (n=4) and all sport experts (n=8). The study employed the Cross-sectional survey design. Data were analyzed by both descriptive and inferential statics methods were employed. Personality and anxiety psychological factors were assessed by using the Inventory, standard questionnaire. Athlete’s athletic performance was assessed by document analysis. Descriptive Statistics percentage, standard deviation, mean were used for assessing back ground information, the current anxiety level and influence personality factors. Multiple regression coefficients from inferential statics were used to show the influence of major psychological factors on athlete’s performance. The finding of this study indicates the current anxiety level with aggregate mean (M=3.147) implies moderate, and influence of personality factors on this study with aggregate mean (M=3.142) shows that moderate. And, also athletic anxiety and personality factors on performance of athletes’ multiple regression coefficient results indicates, by 40.1% of variance (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;=.165, F = 8.194, *p &amp;lt;.05) predict athletes’ performance.

A comparison of patients with epileptic seizures (ES) versus those with psychogenic non-epileptic seizures (PNES) on measures of alexithymia, mood, and anxiety

ObjectiveTo compare persons with epilepsy (PWE) to those with psychogenic non-epileptic seizures (PNES) on measures of depression, anxiety, and alexithymia subscales (i.e., difficulty identifying emotions, difficulty describing emotions, and external-oriented thinking). Material and methodsIn this retrospective study, 235 epilepsy patients and 90 patients with PNES were evaluated between 2012 and 2020 at the Northeast Regional Epilepsy Group. These patients had completed the Toronto Alexithymia Scale (TAS-20), The Center for Epidemiologic Studies - Depression Scale (CES-D) and The State-Trait Anxiety Inventory (STAI). Background information was collected regarding work/student/disability status at the time of the evaluation history of psychiatric diagnosis; psychological trauma; and involvement in psychotherapy either at the time of the evaluation or prior. ResultsSignificant differences between PWEs and those with PNES were found not only in historical data (e.g., Psychiatric History, History of Trauma, and History of Therapy) (p < .001) but also on measures of Depression (p = .002) and Anxiety (p < .001). ANOVA analysis also revealed significant differences in the distribution of the TAS-Total score, TAS-Describing emotions, and TAS-Identifying emotions. Using logistic regression (stepwise model) the optimal set of predictors for a differential diagnosis of epilepsy and PNES was combination of TAS-Identifying emotions score, history of psychological trauma, and history of therapy. The accuracy of the prediction was determined to be 80.2 %. ConclusionsAlthough higher alexithymia rates are present in PNES and PWEs, clinicians may find a combination of TAS-Identifying Emotion score, history of trauma, and history of psychotherapy useful in supporting a differential diagnosis. Also, a subgroup may exist among those with PNES with high levels of alexithymia, depression, and anxiety that may require a different treatment approach focused on addressing difficulties in identifying and describing their emotions and their other symptomatology.

A/C usage intensity and adaptive comfort behaviour in residential buildings in South East Queensland, Australia

Occupants’ interaction with building controls has been recognised as a major contributing factor to the often-reported wide discrepancies between actual and predicted energy consumption in buildings. Accordingly, research into actual patterns of occupant behaviour is increasingly being incorporated into building energy performance simulation tools. However, utilizing a single behaviour pattern can also lead to differences between the actual and predicted energy use in a building. This study aims to classify patterns of occupant adaptive comfort behaviours based on longitudinal field observations made in Australian homes. A sample of 41 households was classified into ‘light’, ‘medium’ and ‘heavy’ usage types according to their annual A/C use. Statistical models describing the proportion of a specific adaptive behaviour (incl. A/C and window operation) in relation to outdoor temperature were developed for the three household classifications. The results indicate that more than 30% of the ‘heavy’ usage households operated their A/C for cooling as the outdoor temperature exceeds 30℃, in contrast to 10% for the ‘medium’ and nearly none for the ‘light’ A/C usage groups. In general, the household A/C usage and hours of windows being open in living rooms were found to be inversely proportional. This study also proposes a probability model for classifying the A/C usage levels of a given dwelling into the three groups (i.e. light, medium and heavy) based on simple background information that can be acquired at the design stage (i.e. site and construction characteristics).

The first microwave and submillimetre closure study using particle models of oriented ice hydrometeors to simulate polarimetric measurements of ice clouds

Abstract. The first closure study involving passive microwave and submillimetre measurements of ice clouds with the consideration of oriented particles is presented, using a unique combination of polarised observations from the ISMAR spectral-like radiometer, two radars with frequencies of 35 and 95 GHz, and a variety of in situ instruments. Of particular interest to this study are the large V–H polarised brightness temperature differences measured from ISMAR above a thick frontal ice cloud. Previous studies combining radar and passive submillimetre measurements have not considered polarisation differences. Moreover, they have assumed particle habits a priori. We aim to test whether the large V–H measurements can be simulated successfully by using an atmospheric model consistent with in situ microphysics. An atmospheric model is constructed using information from the in situ measurements, such as the ice water content, the particle size distribution, and the mass and shape of particles, as well as background information obtained from dropsonde profiles. Columnar and dendritic aggregate particle models are generated specifically for this case, and their scattering properties are calculated using the independent monomer approximation under the assumption of horizontal orientation. The scattering properties are used to perform polarised radiative transfer simulations using ARTS to test whether we can successfully simulate the measured large V–H differences. Radar measurements are used to extrapolate the 1-D microphysical profile to derive a time series of particle size distributions which are used to simulate ISMAR brightness temperatures. These simulations are compared to the observations. It is found that particle models that are consistent with in situ microphysics observations are capable of reproducing the brightness temperature depression and polarisation signature measured from ISMAR at the dual-polarised channel of 243 GHz. However, it was required that a proportion of the particles were changed in order to increase the V–H polarised brightness temperature differences. Thus, we incorporated millimetre-sized dendritic crystals, as these particles were observed in the probe imagery. At the second dual-polarised channel of 664 GHz, the brightness temperature depressions were generally simulated at the correct locations; however, the simulated V–H was too large. This work shows that multi-frequency polarisation information could be used to infer realistic particle shapes, orientations, and representations of the split between single crystals and aggregates within the cloud.

Adaptive Low-Rank Tensor Estimation Model Based Multichannel Weak Fault Detection for Bearings.

Multichannel signals contain an abundance of fault characteristic information on equipment and show greater potential for weak fault characteristics extraction and early fault detection. However, how to effectively utilize the advantages of multichannel signals with their information richness while eliminating interference components caused by strong background noise and information redundancy to achieve accurate extraction of fault characteristics is still challenging for mechanical fault diagnosis based on multichannel signals. To address this issue, an effective weak fault detection framework for multichannel signals is proposed in this paper. Firstly, the advantages of a tensor on characterizing fault information were displayed, and the low-rank property of multichannel fault signals in a tensor domain is revealed through tensor singular value decomposition. Secondly, to tackle weak fault characteristics extraction from multichannel signals under strong background noise, an adaptive threshold function is introduced, and an adaptive low-rank tensor estimation model is constructed. Thirdly, to further improve the accurate estimation of weak fault characteristics from multichannel signals, a new sparsity metric-oriented parameter optimization strategy is provided for the adaptive low-rank tensor estimation model. Finally, an effective multichannel weak fault detection framework is formed for rolling bearings. Multichannel data from the repeatable simulation, the publicly available XJTU-SY whole lifetime datasets and an accelerated fatigue test of rolling bearings are used to validate the effectiveness and practicality of the proposed method. Excellent results are obtained in multichannel weak fault detection with strong background noise, especially for early fault detection.

Exploiting Contextual Target Attributes for Target Sentiment Classification

In the past few years, pre-trained language models (PTLMs) have brought significant improvements to target sentiment classification (TSC). Existing PTLM-based models can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer the classification task to the text/word generation task. Despite the improvements achieved by these models, we argue that they have their respective limitations. For fine-tuning-based models, they cannot make the best use of the PTLMs’ strong language modeling ability because the pre-train task and downstream fine-tuning task are not consistent. For prompting-based models, although they can sufficiently leverage the language modeling ability, it is hard to explicitly model the target-context interactions, which are widely realized as a crucial point of this task. In this paper, we present a new perspective of leveraging PTLM for TSC: simultaneously leveraging the merits of both language modeling and explicit target-context interactions via contextual target attributes. Specifically, we design the domain- and target-constrained cloze test, which can leverage the PTLMs’ strong language modeling ability to generate the given target’s attributes pertaining to the review context. The attributes contain the background and property information of the target, which can help to enrich the semantics of the review context and the target. To exploit the attributes for tackling TSC, we first construct a heterogeneous information graph by treating the attributes as nodes and combining them with (1) the syntax graph automatically produced by the off-the-shelf dependency parser and (2) the semantics graph of the review context, which is derived from the self-attention mechanism. Then we propose a heterogeneous information gated graph convolutional network to model the interactions among the attribute information, the syntactic information, and the contextual information. The experimental results on three benchmark datasets demonstrate the superiority of our model, which achieves new state-of-the-art performance.

Personalized recommendation of entertainment robots in fine arts education based on human–computer interaction and data mining

In the current digital entertainment environment, the art learning mode of human–computer interaction can effectively enhance students’ interest. Entertainment robots, as a novel personalized interaction tool, can provide a more intelligent and entertaining way for art teaching. This article analyzes the personalized recommendation of entertainment robots in art education based on human–computer interaction and data mining.On the basis of collecting a large number of fine art education resources and organizing and labeling them, data mining technology is used to extract user background information and interest preferences. Information retrieval technology is used to filter out content related to user interests from the massive art education resources based on the extracted user interests. We also use recommendation algorithms to sort and personalize the recommended content based on the user’s historical behavior and other user evaluations, in order to improve the accuracy of recommendation results and user satisfaction. The effectiveness and practicality of this personalized recommendation model were verified through user surveys and data analysis. The experimental results indicate that this model can quickly and accurately recommend suitable fine art education content for users, improving their learning effectiveness and experience.

Reprocessable Networks from Vegetable Oils, Salts, and Food Acids: A Green Polymer Outreach Demonstration for Middle School Students.

Massive amounts of mismanaged plastic waste have led to growing concerns about their adverse impacts on the environment, ecosystem, and human health. Enabling efficient plastic recycling is a key component for developing a sustainable future, which requires cohesive efforts in technology innovations, public awareness, and workforce development. Particularly, outreach activities to inform the broader community about current efforts to fabricate sustainable polymeric materials can play a central role in inspiring future generations while also improving their knowledge, viewpoints, and behaviors to address plastic waste challenges. Herein, this account demonstrates an effort to educate middle school students about a key emerging concept in polymer science for sustainable material development: reprocessable polymer networks. Background information is provided to the students about the need to transition from petroleum-based chemical feedstocks to their bioderived counterparts. We note that the materials used in this demonstration lesson are all produced from common household foods, with which students routinely interact in various applications, making them not only safe but also compelling for the middle school classroom.

Knowledge work productivity in an activity-based workplace: a comparative analysis

PurposeThe purpose of this paper was to extend the current knowledge on the impacts of activity-based workplaces (ABWs) on productivity in knowledge work. It offers more background information that is needed to properly evaluate ABWs’ suitability for different organisations. In the results section, ABWs are compared to the cellular and open-plan office types.Design/methodology/approachThis study was conducted using a survey method with 5,841 respondents. The data were collected between 2015 and 2019 from 32 public-sector organisations.FindingsThe findings of this paper reveal that ABWs offer clear advantages over the other office types but requires careful design and well-implemented solutions. The superiority between ABWs and cellular offices also depends on workers’ profiles and needs and is not for everyone.Practical implicationsFor practitioners, this paper offers valuable information to compare the three office types: ABW, cellular and open-plan. It also highlights the importance of careful planning and good implementation, which are both essential to making the ABW environment productive for employees. Finally, this paper clearly provides evidence that ABWs and open-plan offices differ in their facilities and productivity.Originality/valueThis paper included a unique and large sample with open variables, which are fairly rare in ABW-related papers. It also provides evidence that ABWs, when implemented correctly, seem to be an excellent option for some work profiles.

NRENet: Neighborhood removal-and-emphasis network for ship detection in SAR Images

In recent years, object detection in Synthetic-Aperture Radar (SAR) images still faces many challenges, especially for ship detection. Small or dense ships are vulnerable to the interference of complex scenes such as ports and land. In feature extraction, a large amount of redundant information on the feature map will further reduce the network’s attention to small-sized ships. Therefore, in this paper, we propose a network called neighborhood removal-and-emphasis network (NRE-Net), including an object neighborhood removal (ONR) strategy and a neighborhood feature emphasis (NFE) module. Among them, the ONR strategy directly removes the complex background information around the ship before feature extraction, only retaining effective contextual neighborhoods conducive to ship detection and avoiding the interference of complex background information on the network. The NFE module is based on ONR to extract features and form a weight map of small-sized ships or complex images. This module can adaptively recognize the detection neighborhood of each ship and highlight the detection ship on the feature map. Our network has validated the effectiveness of the method on multiple SAR ship datasets and improved the object AP for each size. Our code is available at: https://github.com/Xidian-AIGroup190726/NRENet.

Introduction to the Forum: Assessment of Understudied Languages

Purpose: This introduction provides background information about the Perspectives of the ASHA Special Interest Groups (SIG 14: Cultural and Linguistic Diversity) Forum: Speech and Language Assessment Protocols for Understudied Languages and a preview of the articles published.

Fine-YOLO: A Simplified X-ray Prohibited Object Detection Network Based on Feature Aggregation and Normalized Wasserstein Distance.

X-ray images typically contain complex background information and abundant small objects, posing significant challenges for object detection in security tasks. Most existing object detection methods rely on complex networks and high computational costs, which poses a challenge to implement lightweight models. This article proposes Fine-YOLO to achieve rapid and accurate detection in the security domain. First, a low-parameter feature aggregation (LPFA) structure is designed for the backbone feature network of YOLOv7 to enhance its ability to learn more information with a lighter structure. Second, a high-density feature aggregation (HDFA) structure is proposed to solve the problem of loss of local details and deep location information caused by the necked feature fusion network in YOLOv7-Tiny-SiLU, connecting cross-level features through max-pooling. Third, the Normalized Wasserstein Distance (NWD) method is employed to alleviate the convergence complexity resulting from the extreme sensitivity of bounding box regression to small objects. The proposed Fine-YOLO model is evaluated on the EDS dataset, achieving a detection accuracy of 58.3% with only 16.1 M parameters. In addition, an auxiliary validation is performed on the NEU-DET dataset, the detection accuracy reaches 73.1%. Experimental results show that Fine-YOLO is not only suitable for security, but can also be extended to other inspection areas.

Options for evaluating communication strategies in treatment and clinical trial discussions.

11083 Background: Nationally, participation in oncology clinical trials is low, with lack of representation of women and ethnic and racial minorities. Interventions to increase patient enrollment in clinical trials and to ensure equity are necessary to improve cancer care for all patients. Shared decision-making conversations are in physicians’ control and are amenable to intervention. However, we must understand the current communication strategies used by medical oncologists in their discussions with patients about high-stakes treatment decisions, including participation in clinical trials. Methods: This was an observational qualitative study conducted between Jan 2022 to Feb 2023. We recruited US medical oncologists who routinely treat colon cancer and have access to clinical trials. Oncologists participated in a simulated, recorded telehealth encounter with a standardized patient recently diagnosed with advanced colon cancer and referred for discussion of treatment options, including a possible clinical trial. Oncologists were provided with the same background information on the patient. Four actors, a Black female, Black male, White female, and White male, were trained to represent the same patient with the same values. Results: 107 physicians at 42 academic institutions across 27 states were contacted via email, 47 responded (44%). 26 consented, and 21 completed the study. Participants were a median age of 41 (range 31-71 years), 48% female, 29% Asian, 5% Black. Encounter length ranged from 28:25 to 76:12 minutes (average 44:05 min). One to four treatment options were discussed during the encounter, including standard of care (1 or 2 options), clinical trial, and best supportive care. Clinical trials were explicitly discussed in nearly all (20/21) conversations and were introduced as an option 1st (1/20), 2nd (5/20), 3rd (12/20), 4th (2/20). Clinical trial discussion length ranged from 3:34 to 19:40 min (average 8:30 min). Discussions were shorter with Black vs White patient, average time 7:00 vs 10:00 min, respectively (p = 0.05), female vs male patient was 9:53 vs 7:34 min, respectively (p = 0.12). Recommendations varied from strong recommendation for a clinical trial (9/21), decision deferred to patient (9/21), recommendation for standard of care (3/21). Conclusions: The way oncologists discuss treatment options with their patients powerfully influences the outcome of the conversation and potentially the decision to participate in a clinical trial. In our study, fewer than half of academic GI oncologists recommend a trial. There is significant variability in option talk including the order as well as the content, duration, detail, and recommendations. Clinical trial discussions were shorter for Black patients. A structured approach to this complex decision-making conversation may help oncologists communicate treatment options to patients in a more equitable and effective way.