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Yu X, Zhang L, He Q, Huang Y, Wu P, Xin S, Zhang Q, Zhao S, Sun H, Lei G, Zhang T, Jiang J. Development and validation of an interpretable Markov-embedded multilabel model for predicting risks of multiple postoperative complications among surgical inpatients: a multicenter prospective cohort study. Int J Surg 2024; 110:130-143. [PMID: 37830953 PMCID: PMC10793770 DOI: 10.1097/js9.0000000000000817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 09/18/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND When they encounter various highly related postoperative complications, existing risk evaluation tools that focus on single or any complications are inadequate in clinical practice. This seriously hinders complication management because of the lack of a quantitative basis. An interpretable multilabel model framework that predicts multiple complications simultaneously is urgently needed. MATERIALS AND METHODS The authors included 50 325 inpatients from a large multicenter cohort (2014-2017). The authors separated patients from one hospital for external validation and randomly split the remaining patients into training and internal validation sets. A MARKov-EmbeDded (MARKED) multilabel model was proposed, and three models were trained for comparison: binary relevance, a fully connected network (FULLNET), and a deep neural network. Performance was mainly evaluated using the area under the receiver operating characteristic curve (AUC). The authors interpreted the model using Shapley Additive Explanations. Complication-specific risk and risk source inference were provided at the individual level. RESULTS There were 26 292, 6574, and 17 459 inpatients in the training, internal validation, and external validation sets, respectively. For the external validation set, MARKED achieved the highest average AUC (0.818, 95% CI: 0.771-0.864) across eight outcomes [compared with binary relevance, 0.799 (0.748-0.849), FULLNET, 0.806 (0.756-0.856), and deep neural network, 0.815 (0.765-0.866)]. Specifically, the AUCs of MARKED were above 0.9 for cardiac complications [0.927 (0.894-0.960)], neurological complications [0.905 (0.870-0.941)], and mortality [0.902 (0.867-0.937)]. Serum albumin, surgical specialties, emergency case, American Society of Anesthesiologists score, age, and sex were the six most important preoperative variables. The interaction between complications contributed more than the preoperative variables, and formed a hierarchical chain of risk factors, mild complications, and severe complications. CONCLUSION The authors demonstrated the advantage of MARKED in terms of performance and interpretability. The authors expect that the identification of high-risk patients and the inference of the risk source for specific complications will be valuable for clinical decision-making.
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Affiliation(s)
| | - Luwen Zhang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College
| | - Qing He
- The National Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing
| | - Yuguang Huang
- Department of Anesthesiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences
| | - Peng Wu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College
| | - Shijie Xin
- Department of Vascular and Thyroid Surgery, The First Hospital of China Medical University, Shenyang, Liaoning Province, People’s Republic of China
| | | | - Shengxiu Zhao
- Department of Nursing, Qinghai Provincial People’s Hospital, Xining, Qinghai Province
| | - Hong Sun
- Department of Otolaryngology Head and Neck Surgery
| | - Guanghua Lei
- Department of Orthopedics, Xiangya Hospital of Central South University, Changsha, Hunan Province
| | | | - Jingmei Jiang
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College
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Zhang Y, Hu M, Zhao W, Liu X, Peng Q, Meng B, Yang S, Feng X, Zhang L. A Bibliometric Analysis of Artificial Intelligence Applications in Spine Care. J Neurol Surg A Cent Eur Neurosurg 2024; 85:62-73. [PMID: 36640757 DOI: 10.1055/a-2013-3149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
BACKGROUND With the rapid development of science and technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis of various spine diseases. It has been proved that AI has a broad prospect in accurate diagnosis and treatment of spine disorders. METHODS On May 7, 2022, the Web of Science (WOS) Core Collection database was used to identify the documents on the application of AI in the field of spine care. HistCite and VOSviewer were used for citation analysis and visualization mapping. RESULTS A total of 693 documents were included in the final analysis. The most prolific authors were Karhade A.V. and Schwab J.H. United States was the most productive country. The leading journal was Spine. The most frequently used keyword was spinal. The most prolific institution was Northwestern University in Illinois, USA. Network visualization map showed that United States was the largest network of international cooperation. The keyword "machine learning" had the strongest total link strengths (TLS) and largest number of occurrences. The latest trends suggest that AI for the diagnosis of spine diseases may receive widespread attention in the future. CONCLUSIONS AI has a wide range of application in the field of spine care, and an increasing number of scholars are committed to research on the use of AI in the field of spine care. Bibliometric analysis in the field of AI and spine provides an overall perspective, and the appreciation and research of these influential publications are useful for future research.
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Affiliation(s)
- Yu Zhang
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Man Hu
- Graduate School of Dalian Medical University, Dalian, China
| | - Wenjie Zhao
- Graduate School of Dalian Medical University, Dalian, China
| | - Xin Liu
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Qing Peng
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Bo Meng
- Graduate School of Dalian Medical University, Dalian, China
| | - Sheng Yang
- Graduate School of Dalian Medical University, Dalian, China
| | - Xinmin Feng
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
| | - Liang Zhang
- Department of Orthopedics, Clinical Medical College of Yangzhou University, Yangzhou, China
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Huang RW, Tsai TY, Hsieh YH, Hsu CC, Chen SH, Lee CH, Lin YT, Kao HK, Lin CH. Reliability of Postoperative Free Flap Monitoring with a Novel Prediction Model Based on Supervised Machine Learning. Plast Reconstr Surg 2023; 152:943e-952e. [PMID: 36790782 DOI: 10.1097/prs.0000000000010307] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
BACKGROUND Postoperative free flap monitoring is a critical part of reconstructive microsurgery. Postoperative clinical assessments rely heavily on specialty-trained staff. Therefore, in regions with limited specialist availability, the feasibility of performing microsurgery is restricted. This study aimed to apply artificial intelligence in postoperative free flap monitoring and validate the ability of machine learning in predicting and differentiating types of postoperative free flap circulation. METHODS Postoperative data from 176 patients who received free flap surgery were prospectively collected, including free flap photographs and clinical evaluation measures. Flap circulation outcome variables included normal, arterial insufficiency, and venous insufficiency. The Synthetic Minority Oversampling Technique plus Tomek Links (SMOTE-Tomek) was applied for data balance. Data were divided into 80%:20% for model training and validation. Shapley Additive Explanations were used for prediction interpretations of the model. RESULTS Of 805 total included flaps, 555 (69%) were normal, 97 (12%) had arterial insufficiency, and 153 (19%) had venous insufficiency. The most effective prediction model was developed based on random forest, with an accuracy of 98.4%. Temperature and color differences between the flap and the surrounding skin were the most significant contributing factors to predict a vascular compromised flap. CONCLUSIONS This study demonstrated the reliability of a machine-learning model in differentiating various types of postoperative flap circulation. This novel technique may reduce the burden of free flap monitoring and encourage the broader use of reconstructive microsurgery in regions with a limited number of staff specialists.
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Affiliation(s)
- Ren-Wen Huang
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Tzong-Yueh Tsai
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Yun-Huan Hsieh
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Chung-Chen Hsu
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Shih-Heng Chen
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Che-Hsiung Lee
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Yu-Te Lin
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Huang-Kai Kao
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
| | - Cheng-Hung Lin
- From the Department of Plastic and Reconstructive Surgery, Chang Gung Memorial Hospital, Chang Gung Medical College and Chang Gung University
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Ali SR, Strafford H, Dobbs TD, Fonferko-Shadrach B, Lacey AS, Pickrell WO, Hutchings HA, Whitaker IS. Development and validation of an automated basal cell carcinoma histopathology information extraction system using natural language processing. Front Surg 2022; 9:870494. [PMID: 36439548 PMCID: PMC9683031 DOI: 10.3389/fsurg.2022.870494] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 07/11/2022] [Indexed: 01/26/2024] Open
Abstract
Introduction Routinely collected healthcare data are a powerful research resource, but often lack detailed disease-specific information that is collected in clinical free text such as histopathology reports. We aim to use natural Language Processing (NLP) techniques to extract detailed clinical and pathological information from histopathology reports to enrich routinely collected data. Methods We used the general architecture for text engineering (GATE) framework to build an NLP information extraction system using rule-based techniques. During validation, we deployed our rule-based NLP pipeline on 200 previously unseen, de-identified and pseudonymised basal cell carcinoma (BCC) histopathological reports from Swansea Bay University Health Board, Wales, UK. The results of our algorithm were compared with gold standard human annotation by two independent and blinded expert clinicians involved in skin cancer care. Results We identified 11,224 items of information with a mean precision, recall, and F1 score of 86.0% (95% CI: 75.1-96.9), 84.2% (95% CI: 72.8-96.1), and 84.5% (95% CI: 73.0-95.1), respectively. The difference between clinician annotator F1 scores was 7.9% in comparison with 15.5% between the NLP pipeline and the gold standard corpus. Cohen's Kappa score on annotated tokens was 0.85. Conclusion Using an NLP rule-based approach for named entity recognition in BCC, we have been able to develop and validate a pipeline with a potential application in improving the quality of cancer registry data, supporting service planning, and enhancing the quality of routinely collected data for research.
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Affiliation(s)
- Stephen R. Ali
- Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, United Kingdom
- Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, United Kingdom
| | - Huw Strafford
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom
- Health Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | - Thomas D. Dobbs
- Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, United Kingdom
- Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, United Kingdom
| | - Beata Fonferko-Shadrach
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | - Arron S. Lacey
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom
- Health Data Research UK, Data Science Building, Swansea University Medical School, Swansea University, Swansea, United Kingdom
| | - William Owen Pickrell
- Neurology and Molecular Neuroscience Group, Institute of Life Science, Swansea University Medical School, Swansea University, Swansea, United Kingdom
- Department of Neurology, Morriston Hospital, Swansea, United Kingdom
| | - Hayley A. Hutchings
- Patient and Population Health and Informatics Research, Swansea University Medical School, Swansea, United Kingdom
| | - Iain S. Whitaker
- Reconstructive Surgery and Regenerative Medicine Research Centre, Institute of Life Sciences, Swansea University Medical School, Swansea, United Kingdom
- Welsh Centre for Burns and Plastic Surgery, Morriston Hospital, Swansea, United Kingdom
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Kuo RYL, Harrison C, Curran TA, Jones B, Freethy A, Cussons D, Stewart M, Collins GS, Furniss D. Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis. Radiology 2022; 304:50-62. [PMID: 35348381 DOI: 10.1148/radiol.211785] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background Patients with fractures are a common emergency presentation and may be misdiagnosed at radiologic imaging. An increasing number of studies apply artificial intelligence (AI) techniques to fracture detection as an adjunct to clinician diagnosis. Purpose To perform a systematic review and meta-analysis comparing the diagnostic performance in fracture detection between AI and clinicians in peer-reviewed publications and the gray literature (ie, articles published on preprint repositories). Materials and Methods A search of multiple electronic databases between January 2018 and July 2020 (updated June 2021) was performed that included any primary research studies that developed and/or validated AI for the purposes of fracture detection at any imaging modality and excluded studies that evaluated image segmentation algorithms. Meta-analysis with a hierarchical model to calculate pooled sensitivity and specificity was used. Risk of bias was assessed by using a modified Prediction Model Study Risk of Bias Assessment Tool, or PROBAST, checklist. Results Included for analysis were 42 studies, with 115 contingency tables extracted from 32 studies (55 061 images). Thirty-seven studies identified fractures on radiographs and five studies identified fractures on CT images. For internal validation test sets, the pooled sensitivity was 92% (95% CI: 88, 93) for AI and 91% (95% CI: 85, 95) for clinicians, and the pooled specificity was 91% (95% CI: 88, 93) for AI and 92% (95% CI: 89, 92) for clinicians. For external validation test sets, the pooled sensitivity was 91% (95% CI: 84, 95) for AI and 94% (95% CI: 90, 96) for clinicians, and the pooled specificity was 91% (95% CI: 81, 95) for AI and 94% (95% CI: 91, 95) for clinicians. There were no statistically significant differences between clinician and AI performance. There were 22 of 42 (52%) studies that were judged to have high risk of bias. Meta-regression identified multiple sources of heterogeneity in the data, including risk of bias and fracture type. Conclusion Artificial intelligence (AI) and clinicians had comparable reported diagnostic performance in fracture detection, suggesting that AI technology holds promise as a diagnostic adjunct in future clinical practice. Clinical trial registration no. CRD42020186641 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Cohen and McInnes in this issue.
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Affiliation(s)
- Rachel Y L Kuo
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Conrad Harrison
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Terry-Ann Curran
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Benjamin Jones
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Alexander Freethy
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - David Cussons
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Max Stewart
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Gary S Collins
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
| | - Dominic Furniss
- From the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, Old Road Headington, Oxford OX3 7LD, UK (R.Y.L.K., C.H., M.S., G.S.C., D.F.); Department of Plastic Surgery, John Radcliffe Hospital, Oxford, UK (T.A.C., A.F.); Department of Vascular Surgery, Royal Berkshire Hospital, Reading, UK (B.J.); Department of Plastic Surgery, Stoke Mandeville Hospital, Aylesbury, Buckinghamshire UK (D.C.); and UK EQUATOR Center, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford Centre for Statistics in Medicine, Oxford UK (G.S.C.)
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