Literatura científica selecionada sobre o tema "Pain network"
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Artigos de revistas sobre o assunto "Pain network"
Zheng, Weihao, Choong-Wan Woo, Zhijun Yao, Pavel Goldstein, Lauren Y. Atlas, Mathieu Roy, Liane Schmidt et al. "Pain-Evoked Reorganization in Functional Brain Networks". Cerebral Cortex 30, n.º 5 (9 de dezembro de 2019): 2804–22. http://dx.doi.org/10.1093/cercor/bhz276.
Texto completo da fonteMeier, Sarah K., Kimberly L. Ray, Noah C. Waller, Barry C. Gendron, Semra A. Aytur e Donald A. Robin. "Network Analysis of Induced Neural Plasticity Post-Acceptance and Commitment Therapy for Chronic Pain". Brain Sciences 11, n.º 1 (23 de dezembro de 2020): 10. http://dx.doi.org/10.3390/brainsci11010010.
Texto completo da fonteCockett, Andrea. "Network on pain management". Paediatric Nursing 14, n.º 4 (maio de 2002): 20. http://dx.doi.org/10.7748/paed.14.4.20.s23.
Texto completo da fonteSingavi, Arun, Guangyu Chen, Nancy Wandersee, Collin Hubler, Amanda M. Brandow, Pippa Simpson, Shi-Jiang Li e Joshua J. Field. "Daily Pain Is Associated with Alterations in Functional Connectivity of the Brain on fMRI in Adults with Sickle Cell Disease". Blood 128, n.º 22 (2 de dezembro de 2016): 3656. http://dx.doi.org/10.1182/blood.v128.22.3656.3656.
Texto completo da fonteMogil, Jeffrey S. "Friends in pain: pain tolerance in a social network". Scandinavian Journal of Pain 18, n.º 3 (26 de julho de 2018): 343–44. http://dx.doi.org/10.1515/sjpain-2018-0072.
Texto completo da fonteHosomi, Koichi, Ben Seymour e Youichi Saitoh. "Modulating the pain network—neurostimulation for central poststroke pain". Nature Reviews Neurology 11, n.º 5 (21 de abril de 2015): 290–99. http://dx.doi.org/10.1038/nrneurol.2015.58.
Texto completo da fonteHuang, Dong, Zhaoqiang Xia, Lei Li, Kunwei Wang e Xiaoyi Feng. "Pain-awareness multistream convolutional neural network for pain estimation". Journal of Electronic Imaging 28, n.º 04 (11 de julho de 2019): 1. http://dx.doi.org/10.1117/1.jei.28.4.043008.
Texto completo da fonteHe, Hui, Lan Hu, Saiying Tan, Yingjie Tang, Mingjun Duan, Dezhong Yao, Guocheng Zhao e Cheng Luo. "Functional Changes of White Matter Are Related to Human Pain Sensitivity during Sustained Nociception". Bioengineering 10, n.º 8 (21 de agosto de 2023): 988. http://dx.doi.org/10.3390/bioengineering10080988.
Texto completo da fonteSeminowicz, David A., e Karen D. Davis. "Pain Enhances Functional Connectivity of a Brain Network Evoked by Performance of a Cognitive Task". Journal of Neurophysiology 97, n.º 5 (maio de 2007): 3651–59. http://dx.doi.org/10.1152/jn.01210.2006.
Texto completo da fontePereira, Naiara Lima, Mirelly Tavares Feitosa Pereira, Gisele de Souza Costa, André Luiz Machado das Neves, Izaura Rodrigues Nascimento e Zilmar Augusto de Souza Filho. "“Body and Soul Pain”". International Journal for Innovation Education and Research 7, n.º 12 (31 de dezembro de 2019): 644–57. http://dx.doi.org/10.31686/ijier.vol7.iss12.2118.
Texto completo da fonteTeses / dissertações sobre o assunto "Pain network"
Rissacher, Daniel J. "Neural network recognition of pain state in EEG recordings". Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/16646.
Texto completo da fonteBoshoff, Susan. "Absenteeism and musculoskeletal pain : an interactive network of variables". Master's thesis, University of Cape Town, 2000. http://hdl.handle.net/11427/3367.
Texto completo da fonteAwang, Mahmud Awang Bulgiba. "Application of statistical and neural network techniques to chest pain diagnosis". Thesis, University of East Anglia, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430583.
Texto completo da fonteSamineni, Vijaya K. "The role of the periaqueductal gray in modulation of acute and chronic pain: Actions of drugs with analgesic properties on periaqueductal gray neuronal network". OpenSIUC, 2013. https://opensiuc.lib.siu.edu/dissertations/701.
Texto completo da fonteRogoz, Katarzyna. "Signaling Mechanisms in the Neuronal Networks of Pain and Itch". Doctoral thesis, Uppsala universitet, Genetisk utvecklingsbiologi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-183255.
Texto completo da fonteEnglish, Amber. "The relationship between pain and arousal: The modulation of noxious sensation by the brain’s alerting network". Thesis, English, Amber (2017) The relationship between pain and arousal: The modulation of noxious sensation by the brain’s alerting network. Honours thesis, Murdoch University, 2017. https://researchrepository.murdoch.edu.au/id/eprint/40612/.
Texto completo da fonteSnyder, Kristian. "Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting". University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255.
Texto completo da fonteAlharbi, Ghaleb. "Evidence-based medicine in neuropathic pain : a systematic review, meta-analysis, sequential analysis and network meta-analysis of randomised controlled trials". Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/55427/.
Texto completo da fonteCazzanelli, Silvia. "Functional ultrasound (fUS) imaging of brain functional connectivity alterations in a mouse model of neuropathic pain : impact of nociceptive symptoms and associated comorbidities". Electronic Thesis or Diss., Université Paris sciences et lettres, 2024. http://www.theses.fr/2024UPSLS010.
Texto completo da fonteNeuropathic pain is an abnormal pain sensation that persists longer than the temporal course of natural healing. It interferes with the patient’s quality of life and leads to several comorbidities, such as anxiety and depression. It has been suggested that chronic pain may result from abnormal and maladaptive neuronal plasticity in the structures known to be involved in pain perception (Bliss et al. 2016). This means that nerve injury would trigger long-term potentiation of synaptic transmission in pain-related areas (Zhuo et al. 2014). Since these regions are also involved in the emotional aspects of pain, our hypothesis is that the aforementioned maladaptive plasticity in these brain areas could constitute a key mechanism for the development of comorbidities such as anxiety and depression.My PhD aimed at testing this working hypothesis, through the study of brain resting state functional connectivity (FC) using functional ultrasound imaging (fUS) in a mouse model of neuropathic pain. FUS is a relatively recent neuroimaging technique that enabled numerous advances in neuroscience, thanks to its high spatio-temporal resolution, its sensitivity, but also its adaptability, allowing studies in anesthetized or awake animals.In a first study, I developed an experimental protocol allowing the brains of awake mice to be imaged in a reproducible manner and with minimal stress and movement artifacts and was also involved in the development of a new algorithm for the analysis of the signals generated by these acquisitions. As this first approach was carried out with a moving linear probe which does not allow the entire brain to be visualized, in a second study, I participated in the development of a new compiled and motorized probe technology.Building on these technological developments, I then used these new approaches to test my neurobiological hypothesis. I undertook two parallel studies in animals anesthetized for one and awake for the second, in which we studied the temporal link between alterations in cerebral FC and the development of neuropathic pain and/or associated comorbidities. To do this, we measured the resting-state functional connectivity (FC) in anesthetized and in awake head-fixed mice, at three time points: I) 2 weeks after induction of neuropathic pain (cuff around the sciatic nerve), II) at 8 weeks post-induction during the emergence of anxiety (8W) and III) at 12 weeks post-induction during the emergence of depression. This longitudinal follow-up has been conducted concurrently on a control group.Our results show significant changes in FC in major pain-related brain regions in accordance with the development of neuropathic pain symptoms. These findings suggest that the pain network undergoes maladaptive plasticity following nerve injury which could contribute to pain chronification. Moreover, the time course of these connectivity alterations between regions of the pain network could be correlated with the subsequent apparition of associated comorbidities
Morabit, Safaa El. "New Artificial Intelligence techniques for Computer vision based medical diagnosis". Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2023. http://www.theses.fr/2023UPHF0013.
Texto completo da fonteThe ability to feel pain is crucial for life, since it serves as an early warning system forpotential harm to the body. The majority of pain evaluations rely on patient reports. Patients who are unable to express their own pain must instead rely on third-party reportsof their suffering. Due to potential observer bias, pain reports may contain inaccuracies. In addition, it would be impossible for people to keep watch around the clock. Inorder to better manage pain, especially in noncommunicative patients, automatic paindetection technologies might be implemented to aid human caregivers and complementtheir service. Facial expressions are used by all observer-based pain assessment systemsbecause they are a reliable indicator of pain and can be interpreted from a distance.Taking into consideration that pain generally generates spontaneous facial behavior,these facial expressions could be used to detect the presence of pain. In this thesis, weanalyze facial expressions of pain in order to address pain estimation. First, we presenta thorough analysis of the problem by comparing numerous common CNN (Convolutional Neural Network) architectures, such as MobileNet, GoogleNet, ResNeXt-50, ResNet18, and DenseNet-161. We employ these networks in two unique modes: standalone and feature extraction. In standalone mode, models (i.e., networks) are utilized to directly estimate pain. In feature extractor mode, "values" from the middle layer are extracted and fed into classifiers like Support Vector Regression (SVR) and Random Forest Regression (RFR).CNNs have achieved significant results in image classification and have achievedgreat success. The effectiveness of Transformers in computer vision has been demonstrated through recent studies. Transformer-based architectures were proposed in the second section of this thesis. Two distinct Transformer-based frameworks were presented to address two distinct pain issues: pain detection (pain vs no pain) and thedistinction between genuine and posed pain. The innovative architecture for binaryidentification of facial pain is based on data-efficient image transformers (Deit). Twodatasets, UNBC-McMaster shoulder pain and BioVid heat pain, were used to fine-tuneand assess the trained model. The suggested architecture is built on Vision Transformers for the detection of genuine and simulated pain from facial expressions (ViT). Todistinguish between Genuine and Posed Pain, the model must pay particular attentionto the subtle changes in facial expressions over time. The employed approach takes intoaccount the sequential aspect and captures the variations in facial expressions. Experiments on the publicly accessible BioVid Heat Pain Database demonstrate the efficacy of our strategy
Livros sobre o assunto "Pain network"
Alexis, Caught, ed. Natural networking: Building your professional network, without pain. North Charleston, SC: CreateSpace, 2014.
Encontre o texto completo da fonteParker, Philip M., e James N. Parker. Chronic pain: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2003.
Encontre o texto completo da fonteParker, Philip M., e James N. Parker. Chest pain: A medical dictionary, bibliography and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2003.
Encontre o texto completo da fonteParker, Philip M., e James N. Parker. Foot pain: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2003.
Encontre o texto completo da fonteParker, James N., e Philip M. Parker. Oxycontin: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.
Encontre o texto completo da fonteParker, James N., e Philip M. Parker. Acetaminophen: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.
Encontre o texto completo da fonteParker, Philip M., e James N. Parker. Motrin: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.
Encontre o texto completo da fonteParker, Philip M., e James N. Parker. Tylenol: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.
Encontre o texto completo da fonteParker, James N., e Philip M. Parker. Pain medications: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health, 2004.
Encontre o texto completo da fonteParker, Philip M., e James N. Parker. Neck pain: A medical dictionary, bibliography, and annotated research guide to Internet references. San Diego, CA: ICON Health Publications, 2004.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Pain network"
Salekin, Md Sirajus, Ghada Zamzmi, Dmitry Goldgof, Peter R. Mouton, Kanwaljeet J. S. Anand, Terri Ashmeade, Stephanie Prescott, Yangxin Huang e Yu Sun. "Attentional Generative Multimodal Network for Neonatal Postoperative Pain Estimation". In Lecture Notes in Computer Science, 749–59. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16437-8_72.
Texto completo da fonteLanza, V., e L. Guglielmo. "Local Area Network Videoconferencing for Continuous Anesthesia Quality Improvement". In Anaesthesia, Pain, Intensive Care and Emergency Medicine — A.P.I.C.E., 843–64. Milano: Springer Milan, 2001. http://dx.doi.org/10.1007/978-88-470-2903-3_82.
Texto completo da fonteMiletic, Katarina, Oleksandra Mykhailova e Jan Treur. "An Adaptive Mental Network Model for Reactions to Social Pain". In Complex Networks & Their Applications X, 619–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93409-5_51.
Texto completo da fonteXu, Xuebin, Meng Lei, Dehua Liu e Muyu Wang. "Pain Expression Recognition Based on Dual-Channel Convolutional Neural Network". In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 35–42. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20738-9_5.
Texto completo da fonteBeser-Robles, María, Vicente Sanchis-Alfonso e Luis Martí-Bonmatí. "Brain Network Functional Connectivity Clinical Relevance and Predictive Diagnostic Models in Anterior Knee Pain Patients". In Anterior Knee Pain and Patellar Instability, 731–43. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09767-6_57.
Texto completo da fonteSubramaniam, Saranya Devi, e Brindha Dass. "An Efficient Convolutional Neural Network for Acute Pain Recognition Using HRV Features". In Advances in Intelligent Systems and Computing, 119–32. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2123-9_9.
Texto completo da fonteXu, Haochen, e Manhua Liu. "A Deep Attention Transformer Network for Pain Estimation with Facial Expression Video". In Biometric Recognition, 112–19. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86608-2_13.
Texto completo da fontePikulkaew, Kornprom, Waraporn Boonchieng e Ekkarat Boonchieng. "Real-Time Pain Detection Using Deep Convolutional Neural Network for Facial Expression and Motion". In Proceedings of Seventh International Congress on Information and Communication Technology, 341–49. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1610-6_29.
Texto completo da fonteBhalla, Parinishtha, Anukriti Verma, Bhawna Rathi, Shivani Sharda e Pallavi Somvanshi. "Exploring Molecular Signatures in Spondyloarthritis: A Step Towards Early Diagnosis". In Proceedings of the Conference BioSangam 2022: Emerging Trends in Biotechnology (BIOSANGAM 2022), 142–55. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-020-6_15.
Texto completo da fonteMamontov, Danila, Iana Polonskaia, Alina Skorokhod, Eugene Semenkin, Viktor Kessler e Friedhelm Schwenker. "Evolutionary Algorithms for the Design of Neural Network Classifiers for the Classification of Pain Intensity". In Lecture Notes in Computer Science, 84–100. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20984-1_8.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Pain network"
Choudhury, Abhinav, Shruti Kaushik e Varun Dutt. "Social-Network Analysis for Pain Medications". In ASONAM '17: Advances in Social Networks Analysis and Mining 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3110025.3110113.
Texto completo da fonteMonwar, Md, e Siamak Rezaei. "Pain Recognition Using Artificial Neural Network". In 2006 IEEE International Symposium on Signal Processing and Information Technology. IEEE, 2006. http://dx.doi.org/10.1109/isspit.2006.270764.
Texto completo da fonteNaufal Mansor, Muhammad, Syahrull Hi-Fi Syam, Muhammad Nazri Rejab e Addzrull Hi-Fi Syam B. "Pain assessment using neural network classifier". In 2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA). IEEE, 2012. http://dx.doi.org/10.1109/msna.2012.6324599.
Texto completo da fonteMonwar, Md M., e S. Rezaei. "Appearance-based Pain Recognition from Video Sequences". In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.247069.
Texto completo da fonteSemwal, Ashish, e Narendra D. Londhe. "Automated Pain Severity Detection Using Convolutional Neural Network". In 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS). IEEE, 2018. http://dx.doi.org/10.1109/ctems.2018.8769123.
Texto completo da fontevon Leupoldt, A., T. Sommer, D. Schoen, HJ Baumann, H. Klose, M. Rosenkranz, B. Dahme e C. Buechel. "Dyspnea and Pain Share Affect-Related Brain Network." In American Thoracic Society 2009 International Conference, May 15-20, 2009 • San Diego, California. American Thoracic Society, 2009. http://dx.doi.org/10.1164/ajrccm-conference.2009.179.1_meetingabstracts.a3692.
Texto completo da fonteCarlini, Lucas, Leonardo Ferreira, Gabriel Coutrin, Victor Varoto, Tatiany Marcondes, Rita Balda, Marina Barros, Ruth Guinsburg e Carlos Thomaz. "Mobile Convolutional Neural Network for Neonatal Pain Assessment". In LatinX in AI at Computer Vision and Pattern Recognition Conference 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai202106258.
Texto completo da fonteSousa, Miguel Angelo de Abreu de, e Thiago Felipe de Jesus Torres. "Modeling of Pain on a FPGA-based Neural Network". In Artificial Intelligence and Applications. Calgary,AB,Canada: ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.793-034.
Texto completo da fonteGuo, Wenqiang, Ziwei Xu, Zhigao Guo, Lingling Mao, Yongyan Hou e Zixuan Huang. "Pain Assessment Using Facial Action Units and Bayesian Network". In 2021 40th Chinese Control Conference (CCC). IEEE, 2021. http://dx.doi.org/10.23919/ccc52363.2021.9550304.
Texto completo da fonteMansor, Muhammad Naufal, Shahryull Hi-Fi Syam Mohd Jamil, Mohd Nazri Rejab e Addzrull Hi-Fi Syam Mohd Jamil. "K-nn algorithm for fast infant pain detection". In 2012 International Symposium on Instrumentation & Measurement, Sensor Network and Automation (IMSNA). IEEE, 2012. http://dx.doi.org/10.1109/msna.2012.6324593.
Texto completo da fonteRelatórios de organizações sobre o assunto "Pain network"
Wu, Boyu, Lei Yang, Chengwei Fu, Gonghui Jian, Yue Zhuo e Hui Xiong. Acupuncture for Acute Low Back Pain: A Network Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, dezembro de 2020. http://dx.doi.org/10.37766/inplasy2020.12.0025.
Texto completo da fonteLai, Chih Chin, e Yu Kang Tu. Effectiveness of Exercise Training for Pain Reduction in Adults With and Without Pain: A Systematic Review and Network Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, novembro de 2023. http://dx.doi.org/10.37766/inplasy2023.11.0019.
Texto completo da fonteGuo, Ji xing, Chang chun Ji, Chao ju Xie, Xiang Rao, Zhang yin Sun, Yu Xing, Rong ni Zhang, Qiang qiang Qu, You peng Dong e Jin sheng Yang. Network Meta-analysis of Various Acupuncture Therapies for Managing Nonspecific Low Back Pain. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, setembro de 2024. http://dx.doi.org/10.37766/inplasy2024.9.0033.
Texto completo da fonteJo, Hyo-Rim, Eun-Ji Noh, Se-Hee Oh, Seong-Kyeong Choi, Won-Suk Sung, Su-ji Choi, Dong-il Kim, Seung-Ug Hong e Eun-Jung Kim. Effectiveness of different acupuncture therapies for neck pain: A systematic review and network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, fevereiro de 2021. http://dx.doi.org/10.37766/inplasy2021.2.0041.
Texto completo da fonteCanellas, Joao Vitor, Fabio Ritto e Paul Tiwana. Comparative efficacy and safety of pharmacological interventions to reduce inflammatory complications after mandibular third molar surgery: a systematic review and network meta-analysis protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, julho de 2021. http://dx.doi.org/10.37766/inplasy2021.7.0069.
Texto completo da fonteAlvitos, Rodrigo, Bruno Teixeira Gonçalves Rodrigues, François Isnaldo Dias Caldeira, João Vitor Canellas, Paulo Jose Medeiros, Emmanuel Silva e Gustavo De Deus. Comparative efficacy of different topical anesthetics to reduce the perception of pain during intraoral anesthesia: A systematic review and network meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, dezembro de 2022. http://dx.doi.org/10.37766/inplasy2022.12.0034.
Texto completo da fonteKwak, Sang Gyu, Yoo Jin Choo, Soyoung Kwak e Min Cheol Chang. Efficacy of Transforaminal, Interlaminar, and Caudal Epidural Injections in Lumbosacral Disc Herniation: A Systematic Review and Network Meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, agosto de 2022. http://dx.doi.org/10.37766/inplasy2022.8.0091.
Texto completo da fonteCanellas, Joao Vitor, Fabio Ritto e Paul Tiwana. Comparative efficacy and safety of different corticosteroids to reduce inflammatory complications after mandibular third molar surgery: a systematic review and network meta-analysis protocol. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, setembro de 2021. http://dx.doi.org/10.37766/inplasy2021.9.0023.
Texto completo da fonteTsai, I.-Chen, e Ke-Vin Chang. Comparative Effectiveness of Different Exercises for Reducing Pain Intensity in Primary Dysmenorrhea: A Network Meta-Analysis of Randomized Controlled Trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, março de 2023. http://dx.doi.org/10.37766/inplasy2023.3.0050.
Texto completo da fonteLin, Wenqian. Which analgesia is better for preventing chronic post-thoracotomy pain syndrome(CPTPS): a Bayesia network meta-analysis. INPLASY - International Platform of Registered Systematic Review Protocols, abril de 2020. http://dx.doi.org/10.37766/inplasy2020.4.0065.
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