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Physiotherapists’ knowledge, attitude and practice of clinical prediction rules in low-back pain

Bulletin of Faculty of Physical Therapy 2024年第1期29卷 1-8页

作者： Mbada, Chidozie Emmanuel Afolabi, Victor Adeboye Oghumu, Saturday N. Gebrye, Tadesse Ademoyegun, Adekola Adje, Mishael Karstens, Sven Fatoye, Francis Department of Health Professions Faculty of Health and Education Manchester Metropolitan University Manchester UK Department of Medical Rehabilitation College of Health Sciences Obafemi Awolowo University Ile-Ife Nigeria Department of Physiotherapy Faculty of Allied Medical Sciences College of Medical Sciences University of Calabar Calabar Nigeria Department of Physiotherapy Osun State University Teaching Hospital Osogbo Nigeria Therapeutic Sciences Department of Computer Science Trier University of Applied Sciences Therapeutic Sciences Schneidershof Trier Germany LUNEX International University of Health Differdange Luxembourg

Clinical prediction rules (CPRs) are mathematical tools that are intended to guide clinicians in clinical decision making or predict a future outcome, but they seem rather unknown, under-utilized, or avoided by clinicians. This study aimed to assess knowledge, attitude, and practice of CPRs in low-back pain (LBP) among physiotherapists. A cross-sectional study involving 45 consenting specialist musculoskeletal physiotherapists from three public-funded teaching hospitals in Nigeria was carried out. An adapted validated questionnaire on facilitators and barriers to CPRs utilization, and a socio-demographic proforma were used to collect data. Descriptive and inferential statistics were employed to analyze data. Alpha level was set at p < 0.05. Respondents were mostly males (71.1%), married (64.4%) and first-degree holders (55.6%). Twenty-eight (62.2%) of the respondents had above-average knowledge of CPRs in LBP. Rates for positive attitude towards, and utilization of CPRs were 37.8% and 15.6%. Knowledge and attitude about CPRs in LBP were not significantly influenced by socio-demographic factors (p > 0.05). However, there was a significant association between the utilization of CPRs and years of experience (χ2 = 10.339 p = 0.016). Most Nigerian physiotherapists had above-average knowledge, but a negative attitude and low utilization of CPRs in LBP. Clinicians’ years of clinical experience influence the usage of CPR. There is a need to incorporate training in CPRs into undergraduate and continuous professional development programmes.

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INFORMED BAYESIAN SURVIVAL ANALYSIS

arXiv

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arXiv 2021年

作者： Bartoš, František Aust, Frederik Haaf, Julia M. Department of Psychological Methods University of Amsterdam Noord-Holland Netherlands Institute of Computer Science Czech Academy of Sciences Prague Czech Republic

Background: We provide an overview of Bayesian estimation, hypothesis testing, and model-averaging and illustrate how they benefit parametric survival analysis. We contrast the Bayesian framework to the currently dominant frequentist approach and highlight advantages, such as seamless incorporation of historical data, continuous monitoring of evidence, and incorporating uncertainty about the true data generating process. methods: We illustrate the application of the outlined Bayesian approaches on an example data set from a colon cancer trial. We assess the performance of Bayesian parametric survival analysis and maximum likelihood survival models with AIC/BIC model selection in fixed-n and sequential designs with a simulation study. Results: In the example data set, the Bayesian framework provided evidence for the absence of a positive treatment effect of adding Cetuximab to FOLFOX6 regimen on disease-free survival in patients with resected stage III colon cancer. Furthermore, the Bayesian sequential analysis would have terminated the trial 10.3 months earlier than the standard frequentist analysis. In a simulation study with sequential designs, the Bayesian framework on average reached a decision in almost half the time required by the frequentist counterparts, while maintaining the same power, and an appropriate false-positive rate. Under model misspecification, the Bayesian framework resulted in higher false-negative rate compared to the frequentist counterparts, which resulted in a higher proportion of undecided trials. In fixed-n designs, the Bayesian framework showed slightly higher power, slightly elevated error rates, and lower bias and RMSE when estimating treatment effects in small samples. We found no noticeable differences for survival predictions. We have made the analytic approach readily available to other researchers in the RoBSA R package. Conclusions: The outlined Bayesian framework provides several benefits when applied to parametric survival ana

关键词： Bayesian networks

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A C0 FINITE ELEMENT METHOD FOR THE BIHARMONIC PROBLEM WITH DIRICHLET BOUNDARY CONDITIONS IN A POLYGONAL DOMAIN

arXiv

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arXiv 2022年

作者： Li, Hengguang Wickramasinghe, Charuka D. Yin, Peimeng Department of Mathematics Wayne State University DetroitMI48202 United States Multiscale Methods and Dynamics Group Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

In this paper, we study the biharmonic equation with Dirichlet boundary conditions in a polygonal domain. In particular, we propose a method that effectively decouples the fourth-order problem into a system of two Poison equations and one Stokes equation, or a system of one Stokes equation and one Poisson equation. It is shown that the solution of each system is equivalent to that of the original fourth-order problem on both convex and non-convex polygonal domains. Two finite element algorithms are in turn proposed to solve the decoupled systems. In addition, we show the regularity of the solutions in each decoupled system in both the Sobolev space and the weighted Sobolev space, and we derive the optimal error estimates for the numerical solutions on both quasi-uniform meshes and graded meshes. Numerical test results are presented to justify the theoretical findings. © 2022, CC BY.

关键词： Poisson equation

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AMBY: A development environment for youth to create conversational agents

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International Journal of Child-computer Interaction 2023年 38卷

作者： Tian, Xiaoyi Kumar, Amit Solomon, Carly E Calder, Kaceja D Katuka, Gloria Ashiya Song, Yukyeong Celepkolu, Mehmet Pezzullo, Lydia Barrett, Joanne Boyer, Kristy Elizabeth Israel, Maya Department of Computer and Information Science and Engineering University of Florida Malachowsky Hall Gainesville Floria 32603 United States School of Teaching and Learning University of Florida 1221 SW 5th Ave Gainesville Floria 32601 United States

Conversational AIs such as Alexa and ChatGPT are increasingly ubiquitous in young people's lives, but these young users are often not afforded the opportunity to learn about the inner workings of these technologies. One of the most powerful ways to foster this learning is to empower youth to create AI that is personally and socially meaningful to them. We have built a novel development environment, AMBY–“AI Made By You”–for youth to create conversational agents. AMBY was iteratively designed with and for youth aged 12–13 through contextual inquiry and usability studies. AMBY is designed to foster AI learning with features that enable users to generate training datasets and visualize conversational flow. We report on results from a two-week summer camp deployment, and contribute design implications for conversational AI authoring tools that empower AI learning for youth. © 2023 Elsevier B.V.

关键词： AI education Artificial intelligence education Conversational agent Conversational AI Development environment K-12 Learning tool Middle school Youth

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More than Extracting "Important" Sentences: the Application of PEGASUS 26

More than Extracting "Important" Sentences: the Application ...

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26th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2021

作者： Yang, Ting-Hao Lu, Ching-Ching Hsu, Wen-Lian National Tsing Hua University Institue of Information Systems and Applications Hsinchu City Taiwan National Tsing Hua University Institute of Taiwan Langauges and Langauge Teaching Hsinchu City Taiwan Asia University Department of Computer Science and Information Engineering Taichung City Taiwan

ISBN: (纸本)9781665408257

Pre-trained language models may reduce the amount of training data required. Among the models, PEGASUS, a recently proposed self-supervised approach, is trained to generate the pseudo-summary given the partially masked document. PEGASUS uses gap sentence generation for summarization. The most important sentences are masked, and then PEGASUS predicts the masked sentences as the output summary. In this study, however, we apply PEGASUS in a novel downstream task. We reformulate the task to generate the masked question part in a primary math word problem. In the past research, PEGASUS has shown good potentials on the few-shot datasets, so we try a smaller set of primary math text problems as well. The fine-tuning dataset sizes used in this study are 1000, 500, 50, 10 samples. Their performance are measured by a non-weighted average of the ROUGE-1, ROUGE-2, and ROUGE-L scores. The results show the outstanding performance of PEGASUS applied in our novel downstream task. © 2021 IEEE.

关键词： Natural language processing systems

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Combining Transformers with Natural Language Explanations

arXiv

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arXiv 2021年

作者： Ruggeri, Federico Lippi, Marco Torroni, Paolo Department of Computer Science and Engineering viale Risorgimento 2 Bologna Italy Department of Sciences and Methods for Engineering via Amendola 2 Reggio Emilia Italy

Many NLP applications require models to be interpretable. However, many successful neural architectures, including transformers, still lack effective interpretation methods. A possible solution could rely on building explanations from domain knowledge, which is often available as plain, natural language text. We thus propose an extension to transformer models that makes use of external memories to store natural language explanations and use them to explain classification outputs. We conduct an experimental evaluation on two domains, legal text analysis and argument mining, to show that our approach can produce relevant explanations while retaining or even improving classification performance. © 2021, CC BY-SA.

关键词： Text processing

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REASONER: an explainable recommendation dataset with comprehensive labeling ground truths 23

REASONER: an explainable recommendation dataset with compreh...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Xu Chen Jingsen Zhang Lei Wang Quanyu Dai Zhenhua Dong Ruiming Tang Rui Zhang Li Chen Wayne Xin Zhao Ji-Rong Wen Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China and Gaoling School of Artificial Intelligence Renmin University of China Beijing China Huawei Noah's Ark Lab China www.ruizhang.info Department of Computer Science Hong Kong Baptist University Hong Kong

Explainable recommendation has attracted much attention from the industry and academic communities. It has shown great potential to improve the recommendation persuasiveness, informativeness and user satisfaction. In the past few years, while a lot of promising explainable recommender models have been proposed, the datasets used to evaluate them still suffer from several limitations, for example, the explanation ground truths are not labeled by the real users, the explanations are mostly single-modal and around only one aspect. To bridge these gaps, in this paper, we build a new explainable recommendation dataset, which, to our knowledge, is the first contribution that provides a large amount of real user labeled multi-modal and multi-aspect explanation ground truths. In specific, we firstly develop a video recommendation platform, where a series of questions around the recommendation explainability are carefully designed. Then, we recruit about 3000 high-quality labelers with different backgrounds to use the system, and collect their behaviors and feedback to our questions. In this paper, we detail the construction process of our dataset and also provide extensive analysis on its characteristics. In addition, we develop a library, where many well-known explainable recommender models are implemented in a unified framework. Based on this library, we build several benchmarks for different explainable recommendation tasks. At last, we present many new opportunities brought by our dataset, which are expected to promote the field of explainable recommendation. Our dataset, library and the related documents have been released at https://***/.

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Retraction Note: Deep learning model for home automation and energy reduction in a smart home environment platform

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Neural Computing and Applications 2024年第28期36卷 17747-17747页

作者： Popa, Dan Pop, Florin Serbanescu, Cristina Castiglione, Aniello Computer Science Department University Politehnica of Bucharest Bucharest Romania National Institute for Research and Development in Informatics (ICI) Bucharest Romania Department of Mathematical Methods and Models University Politehnica of Bucharest Bucharest Romania Department of Computer Science University of Salerno Fisciano Italy

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RAST: A Reward Augmented Model for Fine-Grained Sentiment Transfer 10th

RAST: A Reward Augmented Model for Fine-Grained Sentiment Tr...

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10th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2021

作者： Hu, Xiaoxuan Zhang, Hengtong Zhao, Wayne Xin Li, Yaliang Gao, Jing Wen, Ji-Rong School of Information Renmin University of China Beijing China School of Electrical and Computer Engineering Purdue University West Lafayette United States Gaoling School of Artificial Intelligence Renmin University of China Beijing China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science and Engineering University at Buffalo Buffalo United States Alibaba Group Hangzhou China

ISBN: (纸本)9783030884796

In this paper, we propose a novel model RAST (Reward Augmented Sentiment Transfer) for fine-grained sentiment transfer. Existing methods usually suffer from two major drawbacks, i.e., blurre d sentiment distinction and unsatisfactory content preservation. Considering the above issues, we design two kinds of rewards to better control sentiment and content. Specially, we develop a pairwise comparative discriminator that enforces to generate sentences with clear distinctions for different sentiment intensities. Moreover, we utilize an effective sampling strategy to obtain pseudo-parallel sentences with minor changes on the input sentence to enhance content preservation. Experiments on a benchmark dataset show that the proposed model outperforms several competitive approaches. © 2021, Springer Nature Switzerland AG.

关键词： computers

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Pattern Recognition for Driving Scenario Detection in Real Driving Data 31

Pattern Recognition for Driving Scenario Detection in Real D...

引用

31st IEEE Intelligent Vehicles Symposium, IV 2020

作者： Montanari, Francesco German, Reinhard Djanatliev, Anatoli Department for Processes Methods Tools Virtual Environment Audi Ag Ingolstadt85045 Germany Friedrich-Alexander-University Department of Computer Science 7 Erlangen-Nuremberg91058 Germany

For the scenario-based development and testing of automated and connected driving an unknown huge number of different driving scenarios is needed. In this paper we propose an approach that extracts driving scenarios from real driving data without any requirement of predefinitions or rules. Instead of searching for specific scenarios in the data, we cluster recurring patterns and interpret the resulting clusters as potential scenario groups. The method shows promising results. In the exemplary clustering we are able to detect four main scenario groups and corner cases within the clusters. With an huge amount of data this method could be used in the future to set up a scenario database in an automatic manner. © 2020 IEEE.

关键词： Intelligent systems

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