[This corrects the article DOI: 10.3389/fpubh.2023.1136939.].

EUS interventions have an increasing role in the treatment for hepatobiliary-pancreatic diseases. However, the procedure itself is not frequently performed, needs expertise, and carries a high risk of complications. With these limitations, the hands-on practice model is very important for the endoscopist in training for EUS intervention. There have been various hands-on models for EUS interventions, ranging from in vivo living pig model to all-synthetic model. Although a living model provides realistic sensation, the preparation is complex and increases concerns for zoonotic issues. All-synthetic models are easier to prepare and store but not realistic and still need the room for improvement. Hybrid ex vivo model is more widely available and provides various training procedures but still needs special preparation for the porcine tissue.

UNASSIGNED: The purpose of this review is to summarize the characteristics and applications of current hysteroscopic training models.
UNASSIGNED: We conducted a systematic search of PubMed, Embase, and Cochrane Library for eligible studies published before March 2024. Manual screening of references and citation tracking were also performed.
UNASSIGNED: Reported hysteroscopic training models included virtual reality simulators, non-biological material models, plant tissue models, animal tissue models, and human tissue models. No training model was distinctly superior in terms of realism, haptic feedback, availability of standardized scoring of operations, preparation difficulty, reusability of surgical procedure, and prices. Utilizing any type of models for hysteroscopy simulation training could assist trainees in enhancing relevant knowledge, skills, self-confidence, and comfort, but virtual reality models had an advantage in training capacity.
UNASSIGNED: Each hysteroscopic training model has its advantages and disadvantages. An appropriate training curriculum is needed to efficiently leverage the merits of different models. The realism and training effectiveness of various training models need to be compared using rigorously designed studies and standard evaluation tools.

BACKGROUND: Theories, models and frameworks (TMFs) are useful when implementing, evaluating and sustaining healthcare evidence-based interventions. Yet it can be challenging to identify an appropriate TMF for an implementation project. We developed and tested the usability of an online tool to help individuals who are doing or supporting implementation practice activities to identify appropriate models and/or frameworks to inform their work.
METHODS: We used methods guided by models and evidence on implementation science and user-centered design. Phases of tool development included applying findings from a scoping review of TMFs and interviews with 24 researchers/implementers on barriers and facilitators to identifying and selecting TMFs. Based on interview findings, we categorized the TMFs by aim, stage of implementation, and target level of change to inform the tool\'s algorithm. We then conducted interviews with 10 end-users to test the usability of the prototype tool and administered the System Usability Scale (SUS). Usability issues were addressed and incorporated into the tool.
RESULTS: We developed Find TMF, an online tool consisting of 3-4 questions about the user\'s implementation project. The tool\'s algorithm matches key characteristics of the user\'s project (aim, stage, target change level) with characteristics of different TMFs and presents a list of candidate models/frameworks. Ten individuals from Canada or Australia participated in usability testing (mean SUS score 84.5, standard deviation 11.4). Overall, participants found the tool to be simple, easy to use and visually appealing with a useful output of candidate models/frameworks to consider for an implementation project. Users wanted additional instruction and guidance on what to expect from the tool and how to use the information in the output table. Tool improvements included incorporating an overview figure outlining the tool steps and output, displaying the tool questions on a single page, and clarifying the available functions of the results page, including adding direct links to the glossary and to complementary tools.
CONCLUSIONS: Find TMF is an easy-to-use online tool that may benefit individuals who support implementation practice activities by making the vast number of models and frameworks more accessible, while also supporting a consistent approach to identifying and selecting relevant TMFs.

BACKGROUND: Emergency department crowding continues to threaten patient safety and cause poor patient outcomes. Prior models designed to predict hospital admission have had biases. Predictive models that successfully estimate the probability of patient hospital admission would be useful in reducing or preventing emergency department \"boarding\" and hospital \"exit block\" and would reduce emergency department crowding by initiating earlier hospital admission and avoiding protracted bed procurement processes.
OBJECTIVE: To develop a model to predict imminent adult patient hospital admission from the emergency department early in the patient visit by utilizing existing clinical descriptors (ie, patient biomarkers) that are routinely collected at triage and captured in the hospital\'s electronic medical records. Biomarkers are advantageous for modeling due to their early and routine collection at triage; instantaneous availability; standardized definition, measurement, and interpretation; and their freedom from the confines of patient histories (ie, they are not affected by inaccurate patient reports on medical history, unavailable reports, or delayed report retrieval).
METHODS: This retrospective cohort study evaluated 1 year of consecutive data events among adult patients admitted to the emergency department and developed an algorithm that predicted which patients would require imminent hospital admission. Eight predictor variables were evaluated for their roles in the outcome of the patient emergency department visit. Logistic regression was used to model the study data.
RESULTS: The 8-predictor model included the following biomarkers: age, systolic blood pressure, diastolic blood pressure, heart rate, respiration rate, temperature, gender, and acuity level. The model used these biomarkers to identify emergency department patients who required hospital admission. Our model performed well, with good agreement between observed and predicted admissions, indicating a well-fitting and well-calibrated model that showed good ability to discriminate between patients who would and would not be admitted.
CONCLUSIONS: This prediction model based on primary data identified emergency department patients with an increased risk of hospital admission. This actionable information can be used to improve patient care and hospital operations, especially by reducing emergency department crowding by looking ahead to predict which patients are likely to be admitted following triage, thereby providing needed information to initiate the complex admission and bed assignment processes much earlier in the care continuum.

BACKGROUND: In the health system, hospitals are intricate establishments that offer vital medical services. Their resilience plays a crucial role in mitigating the societal repercussions of disasters. A hospital must possess the capacity to withstand risks, preserve its fundamental structure and operations, and enhance its preparedness by augmenting various capabilities and promptly recovering from the impacts of potential risks. It enables the hospital to attain a heightened level of readiness. Therefore, this study aimed to develop a resilience model tailored for hospitals to navigate crises and disasters effectively.
METHODS: This mixed-method study was conducted in 2023 in three phases: (1) Identification of the factors influencing the organizational resilience of the hospital, (2) Evaluation of the influential factors by an expert panel. (3) Following the standardization process, we administered 371 questionnaires to individuals, such as university staff managers and supervisors, nursing managers, and research unit managers. The sample size was determined by multiplying the components by 10, resulting in 360 (10 * 36). Therefore, we selected a sample size of 371 participants. Structural Equation Modeling (SEM) was employed to examine the causal relationships between variables. These steps were performed using SPSS 25.0 and AMOS 22 software. Finally, we identified and presented the final model. We utilized AMOS 22 and applied the SEM to assess the correlation between the variables, with a significance level of 0.05.
RESULTS: Findings indicate that the appropriate modeling identified five dimensions comprising 36 components. These dimensions include vulnerability, preparedness, support management, responsiveness and adaptability, and recovery after the disaster. The model demonstrates a good fit, as indicated by the X2/d indices with a value of 2.202, a goodness of fit index (GFI) of 0.832, a root mean square error of estimation (RMSEA) of 0.057, an adjusted comparative fit index (CFI) of 0.931, and a smoothed fit index (NFI) of 0.901.
CONCLUSIONS: Enhancing hospital resilience is crucial for effective preparedness and response to accidents and disasters. Developing a localized tool for measuring resilience can help identify vulnerabilities, ensure service continuity, and inform rehabilitation programs. The proposed model is a suitable framework for assessing hospital resilience. Key factors include human resource scarcity, hospital specialization, and trauma center capacity. Hospitals should prioritize efficient resource allocation, information technology infrastructure, in-service training, waste management, and a proactive organizational framework to build resilience. By adopting this approach, hospitals can better respond to crises and disasters, ultimately reducing casualties and improving overall preparedness.

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UNASSIGNED: The timing of cranioplasty (CP) has become a widely debated topic in research, there is currently no unified standard. To this end, we established a outcome prediction model to explore the factors influencing the outcome of early CP. Our aim is to provide theoretical and practical basis for whether patients with skull defects after decompressive craniectomy (DC) are suitable for early CP.
UNASSIGNED: A total of 90 patients with early CP after DC from January 2020 to December 2021 were retrospectively collected as the training group, and another 52 patients with early CP after DC from January 2022 to March 2023 were collected as the validation group. The Nomogram was established to explore the predictive factors that affect the outcome of early CP by Least absolute shrinkage analysis and selection operator (LASSO) regression and Logistic regression analysis. Receiver operating characteristic (ROC) curve was used to evaluate the discrimination of the prediction model. Calibration curve was used to evaluate the accuracy of data fitting, and decision curve analysis (DCA) diagram was used to evaluate the benefit of using the model.
UNASSIGNED: Age, preoperative GCS, preoperative NIHSS, defect area, and interval time from DC to CP were the predictors of the risk prediction model of early CP in patients with skull defects. The area under ROC curve (AUC) of the training group was 0.924 (95%CI: 0.867-0.980), and the AUC of the validation group was 0.918 (95%CI, 0.842-0.993). Hosmer-Lemeshow fit test showed that the mean absolute error was small, and the fit degree was good. The probability threshold of decision risk curve was wide and had practical value.
UNASSIGNED: The prediction model that considers the age, preoperative GCS, preoperative NIHSS, defect area, and interval time from DC has good predictive ability.

UNASSIGNED: To analyze the diagnostic value of quantitative features in multimodal magnetic resonance imaging (MRI) images to construct a radio-omics model for breast cancer.
UNASSIGNED: Ninety-five patients with breast-related diseases from January 2020 to January 2021 were grouped into the benign group (n=57) and malignant group (n=38) according to the pathological findings. All cases were randomized as the training group (n=66) and validation group (n=29) in a 7:3 ratio based on the examination time. All subjects were examined by T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), dynamic contrast enhancement (DCE), and apparent diffusion coefficient (ADC) multimodality MRI. The MRI findings were analyzed against pathological findings. A diagnostic breast cancer radiomics model was constructed. The diagnostic efficacy of the model in the validation group was analyzed, and the diagnostic efficacy was analyzed via the ROC curve.
UNASSIGNED: Fibroadenoma accounted for 49.12% of benign breast diseases, and invasive ductal carcinoma accounted for 73.68% of malignant breast diseases. The sensitivity of T1WI, T2WI, DWI, ADC, and DCE in diagnosing breast cancer was 61.14%, 66.67%, 73.30%, 78.95%, and 85.96%, using the four-fold table method. The area under the curves (AUCs) of T1WI, T2WI, DWI, ADC, and DCE for diagnosing breast cancer were 0.715, 0.769, 0.785, 0.835, and 0.792, respectively. The AUCs of plain scan, diffuse, enhanced, plain scan + diffuse, plain scan + enhanced, enhanced + diffuse, and plain scan + enhanced + diffuse for diagnosing breast cancer were 0.746, 0.798, 0.816, 0.839, 0.890, 0.906, and 0.927, respectively.
UNASSIGNED: The construction of a radio-omics model by quantitative features in multimodal MRI images was valuable in the diagnosis of breast cancer. The value of radio-omics models such as plain scan + enhanced + diffuse was higher than the other models in diagnosing breast cancer and could be widely applied in clinical practice.

BACKGROUND: Autophagy is crucial for controlling the manifestation of tuberculosis. This study intends to discover autophagy-related molecular clusters as biomarkers for discriminating between latent tuberculosis (LTBI) and active tuberculosis (ATB) in children through gene expression profile analysis.
METHODS: The expression of autophagy modulators was examined in pediatric patients with LTBI and ATB utilizing public datasets from the Gene Expression Omnibus (GEO) collection (GSE39939 and GSE39940).
RESULTS: In a training dataset (GSE39939), patients with LTBI and ATB exhibited the expression of autophagy-related genes connected with their active immune responses. Two molecular clusters associated with autophagy were identified. Compared to Cluster 1, Cluster 2 was distinguished through decreased adaptive cellular immune response and enhanced inflammatory activation, according to single-sample gene set enrichment analysis (ssGSEA). Per the study of gene set variation, Cluster 2\'s differentially expressed genes (DEGs) played a role in synthesizing transfer RNA, DNA repair and recombination, and primary immunodeficiency. The peak variation efficiency, root mean square error, and area under the curve (AUC) (AUC = 0.950) were all lowered in random forest models. Finally, a seven-gene-dependent random forest profile was created utilizing the CD247, MAN1C1, FAM84B, HSZFP36, SLC16A10, DTX3, and SIRT4 genes, which performed well against the validation dataset GSE139940 (AUC = 0.888). The nomogram calibration and decision curves performed well in identifying ATB from LTBI.
CONCLUSIONS: In summary, according to the present investigation, autophagy and the immunopathology of TB might be correlated. Furthermore, this investigation established a compelling prediction expression profile for measuring autophagy subtype development risks, which might be employed as possible biomarkers in children to differentiate ATB from LTBI.