The impact of dynamic adaptive teaching model on surgical education.

Objective To investigate the impact of dynamic adaptive teaching model on surgical education. Methods Due to the COVID-19 pandemic in 2020, we adopted dynamic adaptive teaching model in the Department of Breast Surgery, Peking Union Medical College Hospital, which divided the whole curriculum into several individual modules and recombined different modules to accommodate to student's levels and schedules. Meanwhile, adaptive strategy also increased the proportion of online teaching and fully utilized electronic medical resources. The present study included quantitative teaching score (QTS) recorded from January 2020 to June 2020, and used the corresponding data from 2019 as control. The main endpoint was to explore the impact of dynamic adaptive teaching model on overall QTS and its interaction effect with trainer's experience and student category. Results Totally, 20 trainers and 181 trainees were enrolled in the present study. With implementation of dynamic adaptive strategy, the overall QTS decreased dramatically (1.76+/-0.84 vs. 4.91+/-1.15, t=4.85, P=0.005). The impact was consistent irrespective of trainers' experience (high experience trainers: 0.85+/-0.40 vs. 2.12+/-0.44, t=4.98, P=0.004;medium experience trainers: 0.85+/-0.29 vs. 2.06+/-0.53, t=4.51, P=0.006;and low experience trainers: 0.10+/-0.16 vs. 0.44+/-0.22, t=2.62, P=0.047). For resident (including graduate) and undergraduate student teaching, both QTS was lower with dynamic strategy (residents: 0.18+/-0.34 vs. 0.97+/-0.14, t=4.35, P=0.007;undergraduate students 1.57+/-0.55 vs. 3.77+/-1.24, t=3.62, P=0.015), but dynamic strategy was effective for post-doc student subgroup and reached comparable QTS as traditional model (0.00+/-0.00 vs. 0.17+/-0.41, t=1.00, P=0.363). Conclusions Dynamic adaptive teaching strategy could be a useful alternative to traditional teaching model for post-doc students. It could be a novel effective solution for saving teaching resources and providing individualized surgical teaching modality.Copyright © 2023, Peking Union Medical College Hospital. All rights reserved.

Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation

The spread of online misinformation threatens public health, democracy, and the broader society. While professional fact-checkers form the first line of defense by fact-checking popular false claims, they do not engage directly in conversations with misinformation spreaders. On the other hand, non-expert ordinary users act as eyes-on-the-ground who proactively counter misinformation - recent research has shown that 96% counter-misinformation responses are made by ordinary users. However, research also found that 2/3 times, these responses are rude and lack evidence. This work seeks to create a counter-misinformation response generation model to empower users to effectively correct misinformation. This objective is challenging due to the absence of datasets containing ground-truth of ideal counter-misinformation responses, and the lack of models that can generate responses backed by communication theories. In this work, we create two novel datasets of misinformation and counter-misinformation response pairs from in-the-wild social media and crowdsourcing from college-educated students. We annotate the collected data to distinguish poor from ideal responses that are factual, polite, and refute misinformation. We propose MisinfoCorrect, a reinforcement learning-based framework that learns to generate counter-misinformation responses for an input misinformation post. The model rewards the generator to increase the politeness, factuality, and refutation attitude while retaining text fluency and relevancy. Quantitative and qualitative evaluation shows that our model outperforms several baselines by generating high-quality counter-responses. This work illustrates the promise of generative text models for social good - here, to help create a safe and reliable information ecosystem. The code and data is accessible on https://github.com/claws-lab/MisinfoCorrect. © 2023 Owner/Author.

Integrated quantitative evaluation framework of sustainable development – The complex case of the Yangtze River Delta

Sustainable development is central to the current societal functioning, whose complexity demands consideration on a regional scale. However, there are disparate methods to express sustainable development, many of which use qualitative analysis cumbersome for policy-makers. Previous studies focused on environmental, economic, and social impacts without fully considering the regulation mechanisms of the plethora of administrative bodies. To fill this research gap, this research establishes an integrated assessment framework involving four pillars: environment and ecology, society and culture, economics, and governance and policy. Further, indicator systems and quantitative analysis give comparable and objective results. The current study applied them to one of the most economically significant and developed Chinese regions, the Yangtze River Delta. The result shows a dynamic variation in regional sustainability from 2010 to 2019, indicating an annual increase. Although economic and societal development has been increasing steadily, environmental development has stagnated in the past two years, and the influencing policy has fluctuated dramatically. Our analysis was done in Shanghai, Jiangsu, Zhejiang, and Anhui. Even though all regions showed increasing sustainability, we observed an imbalance in regional sustainable development. Achieving a regional approach and enhanced regional coordination in the Yangtze River Delta is imperative and cannot be ignored by local, regional, and national policy-makers. More importantly, this study created a model capable of predicting the impact of the COVID-19 epidemic on regional sustainable development. The model showed that, compared with predicted values, a 6.65% decrease in the integrated sustainability index ensued, attributed to the pandemic in Zhejiang province. © 2022 Elsevier Ltd

The 3D-Dynamic Fire Risk Evaluation Method of Modern Logistics Warehouses: A Modified Gustav Method.

International trade connections with COVID-19 impeding the development of the logistics industry in express delivery, the world has become an inseparable part of daily life. To improve protection competency, there is a need for effective research on logistics warehouse fire accident alarms. The goal of this study is to create a novel fire risk evaluation method for fire safety managers in logistics warehouses. The Gustav method is used to convert a plane model to a stereoscopic model. Hazards to construction, hazards to life, and fire rescue competency are all taken into account. The empirical study used JingDong Gu'an logistics park as a case study, and the evaluation results revealed differences in fire risk levels between the two warehouses. The results show that the transmit warehouse had a higher fire risk level than the sorting warehouse. The method describes the total risk of a warehouse fire. It is appropriate for the various types and processes found in modern logistics warehouses. The results of the developed 3D-Dynamic method demonstrate the model's feasibility and practicability even to laypeople with limited professional knowledge.

Well-Aerated Lung and Mean Lung Density Quantified by CT at Discharge to Predict Pulmonary Diffusion Function 5 Months after COVID-19.

Background: The aim of this study was to explore the predictive values of quantitative CT indices of the total lung and lung lobe tissue at discharge for the pulmonary diffusion function of coronavirus disease 2019 (COVID-19) patients at 5 months after symptom onset. Methods: A total of 90 patients with moderate and severe COVID-19 underwent CT scans at discharge, and pulmonary function tests (PFTs) were performed 5 months after symptom onset. The differences in quantitative CT and PFT results between Group 1 (patients with abnormal diffusion function) and Group 2 (patients with normal diffusion function) were compared by the chi-square test, Fisher's exact test or Mann−Whitney U test. Univariate analysis, stepwise linear regression and logistic regression were used to determine the predictors of diffusion function in convalescent patients. Results: A total of 37.80% (34/90) of patients presented diffusion dysfunction at 5 months after symptom onset. The mean lung density (MLD) of the total lung tissue in Group 1 was higher than that in Group 2, and the percentage of the well-aerated lung (WAL) tissue volume (WAL%) of Group 1 was lower than that of Group 2 (all p < 0.05). Multiple stepwise linear regression identified only WAL and WAL% of the left upper lobe (LUL) as parameters that positively correlated with the percent of the predicted value of diffusion capacity of the lungs for carbon monoxide (WAL: p = 0.002; WAL%: p = 0.004), and multiple stepwise logistic regression identified MLD and MLDLUL as independent predictors of diffusion dysfunction (MLD: OR (95%CI): 1.011 (1.001, 1.02), p = 0.035; MLDLUL: OR (95%CI): 1.016 (1.004, 1.027), p = 0.008). Conclusion: At five months after symptom onset, more than one-third of moderate and severe COVID-19 patients presented with diffusion dysfunction. The well-aerated lung and mean lung density quantified by CT at discharge could be predictors of diffusion function in convalesce.

Assessing Resilience of Urban Transportation Systems Under Serious Epidemic Situation

Aiming at the role of urban transportation systems in the prevention and control of the new crown pneumonia epidemic and emergency support. Based on epidemic prevention and control, this paper introduced the concept of resilience. The change process of system performance was divided into the prevention stage, maintenance stage, and recovery stage. Analyzed the factors affecting urban transportation systems resilience at various stages and the causal relationship between the factors. Assessment indicators of the transportation system resilience was established. Bayesian network (BN) was used to build resilience assessment model of urban transportation systems. And BN was used to evaluate and reason the model. Taking Xi'an as an example, the paper assessed the resilience of Xi'an transportation system. GeNIe was used for causal inference and sensitivity analysis of the network. Identify factors with high sensitivity and propose improvement measures. The results show that the model can quantify the resilience of urban transportation systems during the COVID-19 Pandemic, evaluate the current situation of the systems, and analyze the effects of factors on the resilience, to provide decision support for improving the resilience of urban transportation systems and dealing with epidemic risk. © 2021 SPIE

A pragmatic effectiveness-implementation study comparing trial evidence with routinely collected outcome data for patients receiving the REACH-HF home-based cardiac rehabilitation programme.

BACKGROUND: Cardiac rehabilitation for heart failure continues to be greatly underused worldwide despite being a Class I recommendation in international clinical guidelines and uptake is low in women and patients with mental health comorbidities. METHODS: Rehabilitation EnAblement in CHronic Heart Failure (REACH-HF) programme was implemented in four UK National Health Service early adopter sites ('Beacon Sites') between June 2019 and June 2020. Implementation and patient-reported outcome data were collected across sites as part of the National Audit of Cardiac Rehabilitation. The change in key outcomes before and after the supervised period of REACH-HF intervention across the Beacon Sites was assessed and compared to those of the intervention arm of the REACH-HF multicentre trial. RESULTS: Compared to the REACH-HF multicentre trial, patients treated at the Beacon Site were more likely to be female (33.8% vs 22.9%), older (75.6 vs 70.1), had a more severe classification of heart failure (26.5% vs 17.7%), had poorer baseline health-related quality of life (MLHFQ score 36.1 vs 31.4), were more depressed (HADS score 6.4 vs 4.1) and anxious (HADS score 7.2 vs 4.7), and had lower exercise capacity (ISWT distance 190 m vs 274.7 m). There appeared to be a substantial heterogeneity in the implementation process across the four Beacon Sites as evidenced by the variation in levels of patient recruitment, operationalisation of the REACH-HF intervention and patient outcomes. Overall lower improvements in patient-reported outcomes at the Beacon Sites compared to the trial may reflect differences in the population studied (having higher morbidity at baseline) as well as the marked challenges in intervention delivery during the COVID-19 pandemic. CONCLUSION: The results of this study illustrate the challenges in consistently implementing an intervention (shown to be clinically effective and cost-effective in a multicentre trial) into real-world practice, especially in the midst of a global pandemic. Further research is needed to establish the real-world effectiveness of the REACH-HF intervention in different populations.

Mortality Predictors in Severe SARS-CoV-2 Infection.

Background and Objectives: The severe forms of SARS-CoV-2 pneumonia are associated with acute hypoxic respiratory failure and high mortality rates, raising significant challenges for the medical community. The objective of this paper is to present the importance of early quantitative evaluation of radiological changes in SARS-CoV-2 pneumonia, including an alternative way to evaluate lung involvement using normal density clusters. Based on these elements we have developed a more accurate new predictive score which includes quantitative radiological parameters. The current evolution models used in the evaluation of severe cases of COVID-19 only include qualitative or semi-quantitative evaluations of pulmonary lesions which lead to a less accurate prognosis and assessment of pulmonary involvement. Materials and Methods: We performed a retrospective observational cohort study that included 100 adult patients admitted with confirmed severe COVID-19. The patients were divided into two groups: group A (76 survivors) and group B (24 non-survivors). All patients were evaluated by CT scan upon admission in to the hospital. Results: We found a low percentage of normal lung densities, PaO2/FiO2 ratio, lymphocytes, platelets, hemoglobin and serum albumin associated with higher mortality; a high percentage of interstitial lesions, oxygen flow, FiO2, Neutrophils/lymphocytes ratio, lactate dehydrogenase, creatine kinase MB, myoglobin, and serum creatinine were also associated with higher mortality. The most accurate regression model included the predictors of age, lymphocytes, PaO2/FiO2 ratio, percent of lung involvement, lactate dehydrogenase, serum albumin, D-dimers, oxygen flow, and myoglobin. Based on these parameters we developed a new score (COV-Score). Conclusions: Quantitative assessment of lung lesions improves the prediction algorithms compared to the semi-quantitative parameters. The cluster evaluation algorithm increases the non-survivor and overall prediction accuracy.COV-Score represents a viable alternative to current prediction scores, demonstrating improved sensitivity and specificity in predicting mortality at the time of admission.

COVID-19 diagnosis system by deep learning approaches.

The novel coronavirus disease 2019 (COVID-19) has been a severe health issue affecting the respiratory system and spreads very fast from one human to other overall countries. For controlling such disease, limited diagnostics techniques are utilized to identify COVID-19 patients, which are not effective. The above complex circumstances need to detect suspected COVID-19 patients based on routine techniques like chest X-Rays or CT scan analysis immediately through computerized diagnosis systems such as mass detection, segmentation, and classification. In this paper, regional deep learning approaches are used to detect infected areas by the lungs' coronavirus. For mass segmentation of the infected region, a deep Convolutional Neural Network (CNN) is used to identify the specific infected area and classify it into COVID-19 or Non-COVID-19 patients with a full-resolution convolutional network (FrCN). The proposed model is experimented with based on detection, segmentation, and classification using a trained and tested COVID-19 patient dataset. The evaluation results are generated using a fourfold cross-validation test with several technical terms such as Sensitivity, Specificity, Jaccard (Jac.), Dice (F1-score), Matthews correlation coefficient (MCC), Overall accuracy, etc. The comparative performance of classification accuracy is evaluated on both with and without mass segmentation validated test dataset.

Early prediction of severity in coronavirus disease (COVID-19) using quantitative CT imaging.

PURPOSE: To evaluate whether the extent of COVID-19 pneumonia on CT scans using quantitative CT imaging obtained early in the illness can predict its future severity. METHODS: We conducted a retrospective single-center study on confirmed COVID-19 patients between January 18, 2020 and March 5, 2020. A quantitative AI algorithm was used to evaluate each patient's CT scan to determine the proportion of the lungs with pneumonia (VR) and the rate of change (RAR) in VR from scan to scan. Patients were classified as being in the severe or non-severe group based on their final symptoms. Penalized B-splines regression modeling was used to examine the relationship between mean VR and days from onset of symptoms in the two groups, with 95% and 99% confidence intervals. RESULTS: Median VR max was 18.6% (IQR 9.1-32.7%) in 21 patients in the severe group, significantly higher (P < 0.0001) than in the 53 patients in non-severe group (1.8% (IQR 0.4-5.7%)). RAR was increasing with a median RAR of 2.1% (IQR 0.4-5.5%) in severe and 0.4% (IQR 0.1-0.9%) in non-severe group, which was significantly different (P < 0.0001). Penalized B-spline analyses showed positive relationships between VR and days from onset of symptom. The 95% confidence limits of the predicted means for the two groups diverged 5 days after the onset of initial symptoms with a threshold of 11.9%. CONCLUSION: Five days after the initial onset of symptoms, CT could predict the patients who later developed severe symptoms with 95% confidence.

Prevalence and risk factors for lung involvement on low-dose chest CT (LDCT) in a paucisymptomatic population of 247 patients affected by COVID-19.

BACKGROUND: Low-dose chest CT (LDCT) showed high sensitivity and ability to quantify lung involvement of COVID-19 pneumopathy. The aim of this study was to describe the prevalence and risk factors for lung involvement in 247 patients with a visual score and assess the prevalence of incidental findings. METHODS: For 12 days in March 2020, 250 patients with RT-PCR positive tests and who underwent LDCT were prospectively included. Clinical and imaging findings were recorded. The extent of lung involvement was quantified using a score ranging from 0 to 40. A logistic regression model was used to explore factors associated with a score ≥ 10. RESULTS: A total of 247 patients were analyzed; 138 (54%) showed lung involvement. The mean score was 4.5 ± 6.5, and the mean score for patients with lung involvement was 8.1 ± 6.8 [1-31]. The mean age was 43 ± 15 years, with 121 males (48%) and 17 asymptomatic patients (7%). Multivariate analysis showed that age > 54 years (odds ratio 4.4[2.0-9.6] p < 0.001) and diabetes (4.7[1.0-22.1] p = 0.049) were risk factors for a score ≥ 10. Multivariate analysis including symptoms showed that only age > 54 years (4.1[1.7-10.0] p = 0.002) was a risk factor for a score ≥ 10. Rhinitis (0.3[0.1-0.7] p = 0.005) and anosmia (0.3[0.1-0.9] p = 0.043) were protective against lung involvement. Incidental imaging findings were found in 19% of patients, with a need for follow-up in 0.6%. CONCLUSION: The prevalence of lung involvement was 54% in a predominantly paucisymptomatic population. Age ≥ 55 years and diabetes were risk factors for significant parenchymal lung involvement. Rhinitis and anosmia were protective against LDCT abnormalities.

CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19).

OBJECTIVES: To explore the relationship between the imaging manifestations and clinical classification of COVID-19. METHODS: We conducted a retrospective single-center study on patients with COVID-19 from Jan. 18, 2020 to Feb. 7, 2020 in Zhuhai, China. Patients were divided into 3 types based on Chinese guideline: mild (patients with minimal symptoms and negative CT findings), common, and severe-critical (patients with positive CT findings and different extent of clinical manifestations). CT visual quantitative evaluation was based on summing up the acute lung inflammatory lesions involving each lobe, which was scored as 0 (0%), 1 (1-25%), 2 (26-50%), 3 (51-75%), or 4 (76-100%), respectively. The total severity score (TSS) was reached by summing the five lobe scores. The consistency of two observers was evaluated. The TSS was compared with the clinical classification. ROC was used to test the diagnosis ability of TSS for severe-critical type. RESULTS: This study included 78 patients, 38 males and 40 females. There were 24 mild (30.8%), 46 common (59.0%), and 8 severe-critical (10.2%) cases, respectively. The median TSS of severe-critical-type group was significantly higher than common type (p < 0.001). The ICC value of the two observers was 0.976 (95% CI 0.962-0.985). ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918. The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. CONCLUSIONS: The proportion of clinical mild-type patients with COVID-19 was relatively high; CT was not suitable for independent screening tool. The CT visual quantitative analysis has high consistency and can reflect the clinical classification of COVID-19. KEY POINTS: • CT visual quantitative evaluation has high consistency (ICC value of 0.976) among the observers. The median TSS of severe-critical type group was significantly higher than common type (p < 0.001). • ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918 (95% CI 0.843-0.994). The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. • The proportion of confirmed COVID-19 patients with normal chest CT was relatively high (30.8%); CT was not a suitable screening modality.