Confidence Scores Research Articles

A Curriculum on Advanced Topics in Hypertension for Internal Medicine Residents.

Hypertension (HTN) affects nearly half of US adults. Our multi-institutional survey revealed that Internal Medicine residents lack proficiency in advanced HTN topics. We developed a curriculum to address knowledge gaps in these topics and aimed to assess the effects of the curriculum on residents' confidence, desire for future training, and knowledge in advanced HTN topics. HTN experts taught four advanced topics in HTN: conducting a workup for secondary HTN, managing HTN in chronic kidney disease, managing HTN in patients who are or may become pregnant, and managing hypertensive urgency (severe asymptomatic HTN) in the outpatient setting. The setting of the curriculum was an ambulatory educational half-day, during which residents rotated through small-group sessions dedicated to each HTN topic. We developed pre-, immediate post-, and 8 weeks postcurriculum surveys assessing residents' confidence and desire for future training in the four topics (4-point Likert scales), and multiple-choice quizzes to assess changes in knowledge. We used repeated-measures analysis of variance to compare means between time points for surveys and quizzes. A total of 112 Internal Medicine residents participated in the curriculum. The mean confidence scores for all four topics increased from 1.79 to 2.61 precurriculum to 2.90 to 3.45 immediately postcurriculum (all P < 0.001) and remained higher (2.53-3.18) than precurriculum at 8 weeks postcurriculum (all P < 0.02). The mean desire for future training scores decreased from 2.74 to 2.96 precurriculum to 2.06 to 2.36 immediately postcurriculum (all P < 0.001 except for managing HTN in patients who are or may become pregnant, which was P = 0.17) and remained lower (2.08-2.36) than precurriculum at 8 weeks postcurriculum (all P ≤ 0.003). The mean knowledge score increased from 48% precurriculum to 62% immediate postcurriculum (P < 0.001) and remained higher (55%) than precurriculum at 8 weeks postcurriculum (P = 0.015). A curriculum on advanced HTN topics produced durable gains in confidence and knowledge and partially satisfied the desire for future learning among Internal Medicine residents.

Antimicrobial stewardship and education in optometry

ABSTRACT Clinical relevance Antimicrobial stewardship aims to improve how antibiotics are prescribed by clinicians and used by patients, to ensure safe appropriate use, and to prevent and contain antimicrobial resistance. Background Australian optometrists can prescribe a range of antimicrobial agents, but to what extent they consider antimicrobial stewardship is not known. This study evaluated what final-year optometry students and optometrists understood about antimicrobial stewardship in eye care and considered relevance to oral prescribing and education. Methods A knowledge, attitude, and practice (KAP) questionnaire was developed comprising four sections: i) demographic questions, ii) 10 multiple choice questions regarding Knowledge about antimicrobial agents and likely pathogens, diagnosis, treatment, and preventing resistance, iii) 10 Likert rating scale questions about thoughts and Attitudes, iv) mix of question types (5× MCQ: course of action, 16 Likert scale: confidence) related to Practice (diagnosis, management, and prescribing). Descriptive analyses were conducted (medians, mean±SD, and distributions). Results Thirteen students and 36 optometrists completed the survey. Both groups had a good understanding of microbial resistance and antibiotic prescribing for ocular conditions; average knowledge scores were 9.2 ± 0.7 and 8.7 ± 1.1 for students and practitioners, respectively. Scores were neutral to agree for questions related to the need for more educational programs. The answers of practice-related MCQs had a dichotomy of answers indicating a diversity of opinion, as can be the case in practice. Average total practice confidence scores were approximately 57/80 for students and 68/80 for optometrists. Optometrists and students were very confident in their ability to conduct an appropriate case history and use their knowledge and clinical skills, including slit lamp anterior eye assessment. The lowest confidence scores were for conducting microbial testing, i.e. taking swabs for pathology. Conclusion Student and optometrist knowledge, attitudes, and practice around antimicrobial stewardship were high and in line with other health professions globally. There is scope for increased stewardship training.

Teaching medical students ultrasound-guided needle aspiration of synthetic cysts: effect of a formalin-embalmed cadaver simulation model

BackgroundStandard training for ultrasound-guided cyst needle aspiration is currently performed on live patients during residency. This practice presents risk of iatrogenic injury to patients and provides a high-stress learning environment for medical trainees. Simulation training using synthetic cysts in a formalin-embalmed cadaver model may allow for realistic, practical, and effective training free from patient risk.MethodsThirty first-year medical students viewed an orientation video, then attended a skills workshop to perform cyst needle aspiration in formalin-embalmed cadaver tissue under ultrasound guidance. Participants were randomly assigned to one of three ultrasound-trained instructor-types which included a medical student, clinical anatomist, or an ultrasound fellowship trained emergency medicine physician. After training, participants underwent a 5-min skills test to assess their ability to drain a synthetic cyst independently. Pre- and post-training self-confidence surveys were administered.ResultsUltrasound images of synthetic cysts in formalin-embalmed tissue were clear and realistic in appearance, and sonographic needle visualization was excellent. Participants took an average of 161.5 s and 1.9 attempts to complete the procedure. Two of the 30 participants could not complete the procedure within the time limit. Participants’ self-reported confidence with respect to all aspects of the procedure significantly increased post-training. Mean confidence scores rose from 1.2 (95% CI 0.96 to 1.39) to 4.4 (95% CI 4.09 to 4.53) (P < 0.0001) Procedure time, number of attempts, performance scores, and self-confidence outcomes were not significantly affected by instructor type.ConclusionsThe use of synthetic cysts in formalin-embalmed cadaveric tissue is feasible, realistic, and efficacious for the teaching of ultrasound-guided needle aspiration to novice medical trainees. This simulation training method can be delivered effectively by multiple instructor types and may allow medical trainees to increase their tactical skill and self-confidence prior to performing ultrasound-guided cyst needle aspiration on live patients.

The effect of educational interventions by nurses through Orem's self-care theory on the ability of self-care in myocardial Infarction patients in public sector hospital: A quasi-experimental study.

To assess the impact of educational intervention by nurses guided by Orem's theory to reinforce the self-care abilities of patients experiencing myocardial infarction. The prospective, quasi-experimental case-control study was conducted from September 2020 to April 2021 at Dr. Ruth K.M. Pfau Civil Hospital, Karachi, and comprised myocardial infarction and heart failure patients with comorbidities diabetes mellitus and hypertension. The patients were randomised into experiment group A and control group B. Group A received educational intervention one day before and one day after discharge in line with the American Heart Association guidelines. Two 30-45-minute sessions of seven modules were conducted. Group B received routine information from ward staff. Assessment was done at baseline and first month and second month post-intervention using the Heart Failure Self-Care Index version 6.2. Data was analysed using SPSS 21. Of the 80 patients, 40(50%) were in group A; 27(76.5%) males and 13(32.5%) females. The remaining 40(50%) patients were in group B; 20(50%) males and 20(50%) females. The overall age of the sample ranged 36-65 years and 48(60%) had a previous history of heart failure. The mean score of maintenance, management, confidence and overall self-care were significantly higher in group A compared to group B (p<0.05). Nursing self-care educational intervention based on Orem's theory was found to be highly effective among patients of myocardial infarction and heart failure with respect to their self-efficacy ability.

Unveiling the Potential of ChatGPT and YOLOv7 for Evaluating Children's Emotions Using Their Artistic Expressions.

Recent advancements in large language models (LLMs) have sparked considerable interest in their potential applications across various healthcare domains. One promising prospect is leveraging these generative models to accurately predict children's emotions by combining computer vision and natural language processing techniques. However, understanding children's emotional states based on their artistic expressions is equally crucial. To address this challenge, this paper presents a pipelined architecture comprising YOLOv7 and the powerful GPT-3.5 Turbo language model, where YOLOv7 is employed for object detection using art therapy imaging annotations, while GPT-3.5 interprets the sketches. After rigorously evaluating the proposed framework through a series of comprehensive experiments, we observed that our model achieved high confidence scores for both object detection and emotion interpretation. The robust performance of the proposed framework not only aids in explaining children's art but also provides valuable insights for parents and therapists. This capability enables them to better understand children's emotional states based on their artistic expressions, ultimately facilitating improved support and care.

The influence of care home registration type and size on senior care leader’s confidence to provide palliative and end-of-life care: an explanatory sequential mixed methods study

BackgroundCare home staff are key providers of palliative and end-of-life care. Yet, little is known about how care home characteristics can influence care leader’s confidence in their ability to provide optimal palliative and end-of-life care.AimTo understand the influence of care home registration type (nursing, residential or dual registered) and size on senior care leader’s confidence to provide palliative and end-of-life care.DesignAn explanatory sequential mixed methods study comprising an online cross-sectional survey (including the Palliative Care Self-Efficacy Scale) and qualitative individual interviews. Analysis of survey data used a multivariate logistic regression and qualitative interview data used Framework Analysis. A ‘Following the Thread’ method was undertaken for data integration.Setting/participantsUK care home senior care leaders, purposively sampled by registration type, size and geographical location.ResultsThe online survey (N = 107) results indicated that nursing home senior care leaders had higher confidence scores on the Palliative Care Self-Efficacy Scale than residential care home leaders (aOR: 3.85, 95% CI 1.20-12.31, p = 0.02). Care home size did not show effect when adjusting for registration type (medium - aOR 1.71, 95% CI 0.59–4.97, p = 0.33; large – aOR 0.65, 95% CI 0.18–2.30, p = 0.5). Interviews (n = 27) identified three themes that promote confidence, (1) ‘feelings of preparedness’ stemming from staff expertise and experience and care home infrastructure, (2) ‘partnership working’ with external services as a valued member of the multidisciplinary team, and (3) a shared language developed from end-of-life care guidance.ConclusionCare home senior care leader’s confidence is influenced by care home characteristics, particularly availability of on-site registered nurses and the infrastructure of large care homes. All care home leaders benefit from training, working with external, multidisciplinary teams and use of guidance. However, mechanisms to achieve this differed by care home type and size. Further exploration is needed on successful integration of palliative care services and interventions to enhance confidence in residential care homes.

Game-based training to promote handwashing, handrub and gloving for hospital pharmacy operators

ObjectivesAdherence to handwashing, handrub and gloving procedures is mandatory for safe, aseptic drug compounding in hospital pharmacies. This study measured participants’ satisfaction and effectiveness of a game-based training tool (Handtastic...

What factors influence cellular pathologists' confidence in case reporting?

Histopathology is a challenging interpretive discipline, and the level of confidence a pathologist has in their diagnosis is known to vary, which is conveyed descriptively in pathology reports. There has been little study to accurately quantify pathologists' diagnostic confidence or the factors that influence it. In this study involving sixteen pathologists from six NHS trusts, we assessed diagnostic confidence across multiple variables and four specialties. Each case was reported by four pathologists, with each pathologist reporting each case twice (on light microscopy (LM) and digital pathology (DP)). For each diagnosis, pathologists recorded their confidence on a 7-point Likert scale. This provided 16,187 diagnoses and associated confidence scores for analysis. All variables investigated were found to be significantly predictive of diagnostic confidence, except level of pathologist experience. Confidence was lower for difficult to report cases, cases where there was inter- and intra-pathologist variation in the diagnosis, and cases where the pathologist made an incorrect diagnosis. Confidence was higher, although nominally, for LM diagnoses than DP (rate ratio 1.09 (95% CI 1.01-1.18), p = 0.035), although results indicate pathologists are confident to report on DP. Lowest confidence scores were seen in areas of known diagnostic complexity and cases with quality issues. High confidence in incorrect diagnoses were almost invariably attributed to interpretive diagnostic differences which occurred across both rare and common lesions. The results highlight the value of external quality control schemes and the benefits of selective peer review when reporting.

Effectiveness of a pediatric critical care pilot preceptor program: Improved confidence and competency outcomes among a cohort of professional nurses in Blantyre, Malawi

BackgroundAvailability of high-level pediatric training for nurses in Malawi is limited. To address this gap, a novel pediatric critical care nurse preceptor program was developed and implemented by pediatric nurse specialists. AimEvaluate the effectiveness of a pediatric critical care nurse preceptor program, via change in nurses' knowledge, skills, confidence, and precepting competence. DesignA 12-month pediatric critical care nurse preceptor program with assessments at baseline, end of intensive (3 months), and end of program (6 months). SettingBlantyre, Malawi. ParticipantsNurses with two or more years of pediatric nursing experience (N = 20) nominated by unit managers. MethodsQuantitative data were collected throughout program implementation. Assessments included: (1) multiple choice knowledge test, (2) Objective Structured Clinical Examinations in two areas (vital signs and airway, breathing, circulation, disability, exposure assessments; and blood gas and electrolyte analysis), (3) group simulations (cardiopulmonary resuscitation or respiratory distress), (4) Likert-scale clinical confidence survey, and (5) Likert-scale precepting competence survey. Data were analyzed using repeated measures ANOVA with pairwise comparisons. For Likert-scale surveys, median confidence scores were compared using a Friedman test with post hoc pairwise comparisons using Wilcoxon signed-rank tests. ResultsParticipants demonstrated significant improvement in clinical knowledge (p < .001), vital signs and airway, breathing, circulation, disability, exposure assessment (p = .001), blood gas and electrolyte analysis (p = .001), CPR (p < .001) and respiratory distress (p < .001) simulations, clinical confidence (p = .002), and precepting competence (p = .041). ConclusionThis pediatric critical care nurse preceptor program was effective in improving participants' confidence and competence (knowledge and skills) in pediatric critical care nursing and precepting. Results suggest the program's potential to address the shortage of highly trained pediatric critical care nurses in Malawi. This lays groundwork for refining and expanding preceptorship, ultimately improving pediatric critical care nursing education in resource-limited settings.

Evaluating the impacts of digital ECG denoising on the interpretive capabilities of healthcare professionals.

Electrocardiogram (ECG) interpretation is an essential skill across multiple medical disciplines; yet, studies have consistently identified deficiencies in the interpretive performance of healthcare professionals linked to a variety of educational and technological factors. Despite the established correlation between noise interference and erroneous diagnoses, research evaluating the impacts of digital denoising software on clinical ECG interpretation proficiency is lacking. Forty-eight participants from a variety of medical professions and experience levels were prospectively recruited for this study. Participants' capabilities in classifying common cardiac rhythms were evaluated using a sequential blinded and semi-blinded interpretation protocol on a challenging set of single-lead ECG signals (42 × 10 s) pre- and post-denoising with robust, cloud-based ECG processing software. Participants' ECG rhythm interpretation performance was greatest when raw and denoised signals were viewed in a combined format that enabled comparative evaluation. The combined view resulted in a 4.9% increase in mean rhythm classification accuracy (raw: 75.7% ± 14.5% vs. combined: 80.6% ± 12.5%, P = 0.0087), a 6.2% improvement in mean five-point graded confidence score (raw: 4.05 ± 0.58 vs. combined: 4.30 ± 0.48, P < 0.001), and 9.7% reduction in the mean proportion of undiagnosable data (raw: 14.2% ± 8.2% vs. combined: 4.5% ± 2.4%, P < 0.001), relative to raw signals alone. Participants also had a predominantly positive perception of denoising as it related to revealing previously unseen pathologies, improving ECG readability, and reducing time to diagnosis. Our findings have demonstrated that digital denoising software improves the efficacy of rhythm interpretation on single-lead ECGs, particularly when raw and denoised signals are provided in a combined viewing format, warranting further investigation into the impact of such technology on clinical decision-making and patient outcomes.

SRPM-ST: Sequential retraining and pseudo-labeling in mini-batches for self-training

An impediment to training accurate classifiers in supervised learning is the scarcity of labeled data. In that respect, semi-supervised learning could help by using both labeled and unlabeled data. A specific form of semi-supervised learning is self-training (ST). In its basic form, ST trains an initial classifier using the labeled data to generate pseudo-labels for the unlabeled set. At this point, either the whole set of pseudo-labeled data or a subset of them with some high confidence scores about the generated pseudo-labels is selected. The selected pseudo-labeled data are then used to update the initial classifier. Although this process can be repeated to generate new pseudo-labels for the unlabeled data, it is typically a tacit assumption up to this point that the classifier is updated once all pseudo-labels are generated—a process to which we refer as the full-batch ST (F-ST) regardless of any confidence score-based subset selection. Here, we show that sequential retraining and pseudo-labeling in mini-batches (SRPM) could potentially improve the performance of the classifier with respect to F-ST. Our empirical results show the existence of a data-dependent mini-batch size for SRPM that is optimal in terms of possessing the least error rate. In practice, this parameter could be treated as a hyperparameter to tune.

Detection and tracking of barchan dunes using artificial intelligence

Barchans are crescent-shape dunes ubiquitous on Earth and other celestial bodies, which are organized in barchan fields where they interact with each other. Over the last decades, satellite images have been largely employed to detect barchans on Earth and on the surface of Mars, with AI (Artificial Intelligence) becoming an important tool for monitoring those bedforms. However, automatic detection reported in previous works is limited to isolated dunes and does not identify successfully groups of interacting barchans. In this paper, we inquire into the automatic detection and tracking of barchans by carrying out experiments and exploring the acquired images using AI. After training a neural network with images from controlled experiments where complex interactions took place between dunes, we did the same for satellite images from Earth and Mars. We show, for the first time, that a neural network trained properly can identify and track barchans interacting with each other in different environments, using different image types (contrasts, colors, points of view, resolutions, etc.), with confidence scores (accuracy) above 70%. Our results represent a step further for automatically monitoring barchans, with important applications for human activities on Earth, Mars and other celestial bodies.

The detection of pathological parathyroid glands is facilitated by identifying vascular features on ultrasound: the potential benefit of a low-frequency vascular probe.

To evaluate the potential benefit of adding a low frequency vascular probe to the conventional pre-operative ultrasound examination of patients with primary hyperparathyroidism. A prospective cohort of 136 patients with primary hyperparathyroidism underwent a conventional ultrasound examination of the neck with a high frequency ( > 10 MHz) linear ultrasound probe, followed by an add-on examination with a low frequency vascular probe. For each ultrasound probe, and for every potential parathyroid lesion, the presence of a feeding vessel, a polar placement of the feeding vessel, and the presence of a vascular arch was recorded. A total of 146 ultrasound lesions were evaluated for vascularity by each probe. For both ultrasound probes, the odds of a hyperfunctioning parathyroid gland being correctly identified increased with the number of visible vascular features. The vascular probe identified a significantly higher number of vascular features among ultrasound true positive glands compared with the conventional probe (p < 0.0001). Among histopathologically verified pathological parathyroid glands, the vascular probe identified 20% more feeding vessels, 27% more polar placements of the feeding vessel, and 65% more vascular arches than the high frequency probe. However, the diagnostic confidence score for true positive glands did not differ significantly between the probes (p = 0.11). The addition of a low frequency vascular probe increases the number of visible vascular features in hyperfunctioning parathyroid glands, which facilitates their preoperative detection. Whether or not this can increase the diagnostic confidence of ultrasound examiners has yet to be substantiated.

Effect of simulation-based emergency airway management education on the knowledge, skills and perceived confidence of medical interns.

An effective airway management education program is a crucial part of the undergraduate medical education curriculum. Theoretical instructions and practical demonstrations are the major modalities of medical education in Nepal. Simulation-based education (SBE) programs have not yet been implemented effectively. The authors aimed to determine the effects of an SBE program on the knowledge, skills, and perceived confidence of medical interns regarding emergency airway management. This mixed methods study comprised both quantitative and qualitative components. The study participants were 47 medical interns who had participated in the SBE program. The mean age of the 47 participants was 24.74 years. There were 33 (70.21%) male and 14 (29.79%) female participants. The knowledge, skills, and perceived confidence scores of the participants for airway management preparation, basic airway management, endotracheal intubation, and laryngeal mask airway (LMA) insertion improved significantly following the SBE program (P<0.001). Analysis of the participants' feedback indicated that they largely approved of the SBE program. The majority of students and faculty expressed a willingness to include similar programs in the undergraduate medical education curriculum. This study demonstrated through quantitative and qualitative metrics that SBE can enhance the knowledge, skills, and perceived confidence in performing emergency airway management among medical interns. The authors recommend measures to include and effectively implement SBE in the undergraduate medical education curriculum of Nepal.

Spatial ecosystem monitoring with a Multichannel Acoustic Autonomous Recording Unit (MAARU)

Abstract Multi‐microphone recording adds spatial information to recorded audio with emerging applications in ecosystem monitoring. Specifically placing sounds in space can improve animal count accuracy, locate illegal activity like logging and poaching, track animals to monitor behaviour and habitat use and allow for ‘beamforming’ to amplify sounds from target directions for downstream classification. Studies have shown many advantages of spatial acoustics, but uptake remains limited as the equipment is often expensive, complicated, inaccessible or only suitable for short‐term deployments. With an emphasis on enhanced uptake and usability, we present a low‐cost, open‐source, six‐channel recorder built entirely from commercially available components which can be integrated into a solar‐powered, online system. The MAARU (Multichannel Acoustic Autonomous Recording Unit) works as an independent node in long‐term autonomous, passive and/or short‐term deployments. Here, we introduce MAARU's hardware and software and present the results of lab and field tests investigating the device's durability and usability. MAARU records multichannel audio with similar costs and power demands to equivalent omnidirectional recorders. MAARU devices have been deployed in the United Kingdom and Brazil, where we have shown MAARUs can accurately localise pure tones up to 6 kHz and bird calls as far as 8 m away (±10° range, 100% and &gt;60% of signals, respectively). Louder calls may have even further detection radii. We also show how beamforming can be used with MAARUs to improve species ID confidence scores. MAARU is an accessible, low‐cost option for those looking to explore spatial acoustics accurately and easily with a single device, and without the formidable expenses and processing complications associated with establishing arrays. Ultimately, the added directional element of the multichannel recording provided by MAARU allows for enhanced recording of sonic environments, further enabling a potential step change in the uptake of spatial acoustics in the wider field.

An Enhanced Adaptive Medical Diagnostic Model Driven By Genetic–Neuro-Fuzzy Algorithms

This research aims at developing adaptive medical diagnostic system driven by genetic, neural network and fuzzy logic algorithms for effective diagnosis of tuberculosis. Medical diagnosis for tuberculosis disease is a complex decision process that involves a lot of vagueness and uncertainty management, since the disease has multiple symptoms. The use of several algorithms has been explored in clinical diagnosis models for diagnosing and prescribing therapy for tuberculosis, but has challenges in transforming the vagueness and uncertainty of multiple symptoms due to the noisy nature of the disease data and over fitting of the models, this had led to the challenge of accurate classification, training, optimization, diagnosis and prescription of therapy. Object Oriented Analysis and Design (OOAD) and System Structured Analysis and Design (SSAD) methodologies were adopted in the research. The methodology involved obtaining four hundred and thirty (430) clinical data from patients with tuberculosis records at the Tuberculosis and Leprosy Hospital, Eku, Delta State. The obtained data were pre-processed using missing values imputation and numeric data encoding methods. Thereafter, a genetic algorithm was applied to the processed data to select six relevant features from the initial number of 16 features (Cough, Night sweats, Fever, Systolic Blood Pressure, Difficulty in Breathing, Loss of appetite, Sputum, Chills, Loss of pleasure, Immune Suppression, Chest Pain, Lack of concentration, Irritation, Loss of energy, Lymph Node Enlargement, Body Mass Index). The reduced dataset was split into training and test sets. The ANFIS (Adaptive Neuro-Fuzzy Inference System) was applied to perform diagnosis in which it was trained and validated on training and test sets respectively. The ANFIS was driven by Mamdani’s inference mechanism with sixty-four (64) generated rules with confidence and support scores of above 10% and 15% respectively. The developed model performance on the test sets gave an accuracy of 90% precision, sensitivity, and specificity of 100%. The results showed that all the attributes of tuberculosis contributed to the degree of the diseases based on their respective weights. Conclusively, the system accurately classified, trained attributes, and predicts the severity of tuberculosis.

CLIP-SP: Vision-language model with adaptive prompting for scene parsing

We present a novel framework, CLIP-SP, and a novel adaptive prompt method to leverage pre-trained knowledge from CLIP for scene parsing. Our approach addresses the limitations of DenseCLIP, which demonstrates the superior image segmentation provided by CLIP pre-trained models over ImageNet pre-trained models, but struggles with rough pixel-text score maps for complex scene parsing. We argue that, as they contain all textual information in a dataset, the pixel-text score maps, i.e., dense prompts, are inevitably mixed with noise. To overcome this challenge, we propose a two-step method. Firstly, we extract visual and language features and perform multi-label classification to identify the most likely categories in the input images. Secondly, based on the top-k categories and confidence scores, our method generates scene tokens which can be treated as adaptive prompts for implicit modeling of scenes, and incorporates them into the visual features fed into the decoder for segmentation. Our method imposes a constraint on prompts and suppresses the probability of irrelevant categories appearing in the scene parsing results. Our method achieves competitive performance, limited by the available visual-language pre-trained models. Our CLIP-SP performs 1.14% better (in terms of mIoU) than DenseCLIP on ADE20K, using a ResNet-50 backbone.

Confidence Prediction Based on Uncertain Knowledge Graph Structure Embedding

Abstract The development of large-scale knowledge graphs (KGs) has given rise to uncertain relational facts, leading to research on uncertain knowledge graph (KG) embeddings. While various studies have been conducted on the task of uncertain KG embeddings, they often employ simplistic scoring functions based on the internal interaction information among triplets to fit confidence scores, neglecting the rich neighborhood information. In light of this, we propose a novel model UKGSE for uncertain KG embeddings that captures the subgraph structural features formed by the neighbors of triplets, aiming to predict confidence scores for triplets. To validate the effectiveness of our model, we conduct confidence prediction tasks on benchmark datasets. The experimental results indicate that the performance of our proposed model surpasses mainstream embedding methods.

Youth Mental Health First Aid Training: Impact on the Ability to Recognize and Support Youth Needs

Youth Mental Health First Aid (YMHFA) trains individuals who regularly interact with youth to identify youth experiencing mental health challenges. Several studies demonstrate positive training impacts, but few assess whether the training equally impacts participants of different demographic and professional backgrounds or those who participate in different training modalities. Using a pre-post follow-up design with a comparison group, this study examined changes in participants’ confidence in their ability to recognize and support youth mental health needs 1 to 2 months after training. Data were collected over two years (2021–2023) from training participants (n = 480) and comparable non-participants (n = 51). The authors examined whether changes in confidence varied by participant race/ethnicity, professional role in the education or mental health fields, and training modality (online versus hybrid). Training participants’ confidence in supporting youth mental health increased significantly compared to non-participants. Although the training was effective for all participants, those with less mental health experience benefited more, consistent with previous research. While both in-person and hybrid training were effective, in-person training participants reported slightly higher confidence scores than virtual at follow-up. Study findings suggest that educational and social service organizations should offer this training to their staff and community members who interact with youth, prioritizing participants with less prior mental health training and delivering training through an in-person training modality when possible. However, additional research is needed to explore how aspects of in-person training, such as trainer characteristics and group dynamics, impact outcomes.

Impact of database choice and confidence score on the performance of taxonomic classification using Kraken2

AbstractAccurate taxonomic classification is essential to understanding microbial diversity and function through metagenomic sequencing. However, this task is complicated by the vast variety of microbial genomes and the computational limitations of bioinformatics tools. The aim of this study was to evaluate the impact of reference database selection and confidence score (CS) settings on the performance of Kraken2, a widely used k-mer-based metagenomic classifier. In this study, we generated simulated metagenomic datasets to systematically evaluate how the choice of reference databases, from the compact Minikraken v1 to the expansive nt- and GTDB r202, and different CS (from 0 to 1.0) affect the key performance metrics of Kraken2. These metrics include classification rate, precision, recall, F1 score, and accuracy of true versus calculated bacterial abundance estimation. Our results show that higher CS, which increases the rigor of taxonomic classification by requiring greater k-mer agreement, generally decreases the classification rate. This effect is particularly pronounced for smaller databases such as Minikraken and Standard-16, where no reads could be classified when the CS was above 0.4. In contrast, for larger databases such as Standard, nt and GTDB r202, precision and F1 scores improved significantly with increasing CS, highlighting their robustness to stringent conditions. Recovery rates were mostly stable, indicating consistent detection of species under different CS settings. Crucially, the results show that a comprehensive reference database combined with a moderate CS (0.2 or 0.4) significantly improves classification accuracy and sensitivity. This finding underscores the need for careful selection of database and CS parameters tailored to specific scientific questions and available computational resources to optimize the results of metagenomic analyses.