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861 results on '"Khan, Moin"'

3. Opioid reduction and enhanced recovery in orthopaedic surgery (OREOS): a protocol for a feasibility randomised controlled trial in patients undergoing total knee arthroplasty

Author

Madden, Kim, Pallapothu, Sushmitha, Young Shing, Darren, Adili, Anthony, Bhandari, Mohit, Carlesso, Lisa, Khan, Moin, Kleinlugtenbelt, Ydo V., Krsmanovic, Adrijana, Nowakowski, Matilda, Packham, Tara, Romeril, Eric, Tarride, Jean-Eric, Thabane, Lehana, Tushinski, Daniel M., Wallace, Christine, Winemaker, Mitchell, and Shanthanna, Harsha

Published
2024
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4. Mortality, perioperative complications and surgical timelines in hip fracture patients: Comparison of the Spanish with the non-Spanish Cohort of the HIP ATTACK-1 trial

Author

Veevaete, Laurent, Waroux, Bernard le Polain de, Lavand'homme, Patricia, Cornu, Olivier, Tribak, Karim, Yombi, Jean C, Touil, Nassim, Bhutia, Jigme T, Clinckaert, Carol, Clippeleir, Dirk De, Reul, Maike, Patel, Ameen, Tandon, Vikas, Gauthier, Leslie P, Avram, Victoria R.A., Winemaker, Mitchell, de Beer, Justin, Simpson, Diane L, Worster, Andrew, Alvarado, Kim A, Gregus, Krysten K, Lawrence, Kelly H, Leong, Darryl P, Joseph, Philip G, Magloire, Patrick, Deheshi, Benjamin, Bisland, Stuart, Wood, Thomas J, Tushinski, Daniel M., Wilson, David A.J., Haider, Shariq, Bali, Kamal, Kearon, Clive, Manach, Yannick Le, Adili, Anthony, Tiboni, Maria E, Neary, John D, Cowan, David D, Khanna, Vickas, Zaki, Amna, Farrell, Janet C, MacDonald, Anne Marie, Conen, David, Wong, Steven C.W., Karbassi, Arsha, Wright, Douglas S, Shanthanna, Harsha, Coughlin, Ryan, Khan, Moin, Wikkerink, Spencer, Quraishi, Faraaz A, Lawendy, Abdel-Rahman, Kishta, Waleed, Schemitsch, Emil, Carey, Timothy, Macleod, Mark D, Sanders, David W, Vasarhelyi, Edward, Bartley, Debra, Dresser, George K, Tieszer, Christina, Jenkinson, Richard J, Shadowitz, Steven, Lee, Jacques S, Choi, Stephen, Kreder, Hans J, Nousiainen, Markku, Kunz, Monica R, Tuazon, Ravianne, Shrikumar, Mopina, Ravi, Bheeshma, Wasserstein, David, Stephen, David J.G., Nam, Diane, Henry, Patrick D.G., Wood, Gavin C.A., Mann, Stephen M., Jaeger, Melanie T., Sivilotti, Marco L.A., Smith, Christopher A., Frank, Christopher C., Grant, Heather, Ploeg, Leone, Yach, Jeff D., Harrison, Mark M., Campbell, Aaron R., Bicknell, Ryan T., Bardana, Davide D., Feibel, Robert J., McIlquham, Katie, Gallant, Catherine, Halman, Samantha, Thiruganasambandamoorth, Venkatesh, Ruggiero, Sara, Hadden, William J., Chen, Brian P.J., Coupal, Stephanie A., McMahon, Stephen J., McLean, Lisa M., Shirali, Hemant R., Haider, Syed Y., Smith, Crystal A., Watts, Evan, Santone, David J., Koo, Kevin, Yee, Allan J., Oyenubi, Ademilola N., Nauth, Aaron, Schemitsch, Emil H., Daniels, Timothy R., Ward, Sarah E., Hall, Jeremy A., Ahn, Henry, Whelan, Daniel B., Atrey, Amit, Khoshbin, Amir, Puskas, David, Droll, Kurt, Cullinan, Claude, Payendeh, Jubin, Lefrancois, Tina, Mozzon, Lise, Marion, Travis, Jacka, Michael J., Greene, James, Menon, Matthew, Stiegelmahr, Robert, Dillane, Derek, Irwin, Marleen, Beaupre, Lauren, Coles, Chad P., Trask, Kelly, MacDonald, Shelley, Trenholm, J.A.I., Oxner, William, Richardson, C.G., Dehghan, Niloofar, Sadoughi, Mehdi, Sharma, Achal, White, Neil J., Olivieri, Loretta, Hunt, Stephen B., Turgeon, Thomas R., Bohm, Eric R., Tran, Sarah, Giilck, Stephen M., Hupel, Tom, Guy, Pierre, O'Brien, Peter J., Duncan, Andrew W., Crawford, Gordon A., hou, Junlin, Zhao, Yanrui, Liu, Yang, Shan, Lei, Wu, Anshi, Muñoz, Juan M., Chaudier, Philippe, Douplat, Marion, Fessy, Michel Henri, Piriou, Vincent, Louboutin, Lucie, David, Jean Stephane, Friggeri, Arnaud, Beroud, Sebastien, Fayet, Jean Marie, Landais, Paul, Leung, Frankie Ka Li, Fang, Christian Xinshuo, Yee, Dennis King Hang, Sancheti, Parag K., Pradhan, Chetan V., Patil, Atul A., Puram, Chetan P., Borate, Madhav P., Kudrimoti, Kiran B., Adhye, Bharati A., Dongre, Himanshu V., John, Bobby, Abraham, Valsamma, Pandey, Ritesh A., Rajkumar, Arti, George, Preetha E., Sigamani, Alen, Stephen, Manesh, Chandran, Nitheesh, Ashraf, Mohammed, Georgekutty, A.M., Sulthan, Ahamad S., Adinarayanan, S., Sharma, Deep, Barnawal, Satish P., Swaminathan, Srinivasan, Bidkar, Prasanna U., Mishra, Sandeep K., Menon, Jagdish, M, Niranjan, Zk, Varghese, Hiremath, Santosh A., Nc, Madhusudhan, Jawali, Abhijit, Gnanadurai, Kingsly R., George, Carolin E., Maddipati, Atarao, Kp, Mary, Sharma, Vijay, Farooque, Kamran, Malhotra, Rajesh, Mittal, Samarth, Sawhney, Chavi, Gupta, Babita, Mathur, Purva, Gamangati, Shivanand, Tripathy, Vijaylaxmi, Menon, Prem H., Dhillon, Mandeep S., Chouhan, Devendra K., Patil, Sharanu, Narayan, Ravi, Lal, Purushotham, Bilchod, Prashanth N., Singh, Surya U., Gattu, Uttam V., Dashputra, Ravi P., Rahate, Prashant V., Turiel, Maurizio, Blasio, Giuseppe De, Accetta, Riccardo, Perazzo, Paolo, Stella, Daniele, Bonadies, Marika, Colombo, Chiara, Fozzato, Stefania, Pino, Fabio, Morelli, Ilaria, Colnaghi, Eleonora, Salini, Vincenzo, Denaro, Giuseppe, Beretta, Luigi, Placella, Giacomo, Giardina, Giuseppe, Binda, Mirko, Marcato, Anna, Guzzetti, Luca, Piccirillo, Fabio, Cecconi, Maurizio, Khor, H.M., Lai, Hou Yee, Kumar, C.S., Chee, K.H., Loh, P.S., Tan, Kit Mun, Singh, Simmrat, Foo, Li Lian, Prakasam, Komella, Chaw, Sook Hui, Lee, Meng-Li, Ngim, Joanne H.L., Boon, Huck Wee, Chin, Im Im, Kleinlugtenbelt, Ydo V., Landman, Ellie B.M., Flikweert, Elvira R., Roerdink, Herbert W., Brokelman, Roy B.G., Elskamp-Meijerman, Hannie F., Horst, Maarten R., Cobben, Jan-Hein M.G., Umer, Masood, Begum, Dilshad, Anjum, Anila, Hashmi, Pervaiz M., Ahmed, Tashfeen, Rashid, Haroon U., Khattak, Mujahid J., Rashid, Rizwan H., Lakdawala, Riaz H., Noordin, Shahryar, Juman, Naveed M., Khan, Robyna I., Riaz, Muhammad M., Bokhari, Syedah S., Almas, Ayesha, Wahab, Hussain, Ali, Arif, Khan, Hammad N., Khan, Eraj K., Nur, Aamer Nabi, Janjua, Kholood A., Orakzai, Sajjad H., Khan, Abdus S., Mustafa, Khawaja J., Sohail, Mian A., Umar, Muhammad, Khan, Siddra A., Ashraf, Muhammad, Khan, Muhammad K., Shiraz, Muhammad, Furgan, Ahmad, Ślęczka, Paweł, Dąbek, Piotr, Kumoń, Adam, Satora, Wojciech, Ambroży, Wojciech, Święch, Mariusz, Rycombel, Jacek, Grzelak, Adrian, Nowak- Kózka, Ilona, Gucwa, Jaroslaw, Machala, Waldemar, Ramokgopa, Mmampapatla T., Firth, Gregory B., Karera, Mwalimu, Fourtounas, Maria, Singh, Virsen, Biscardi, Anna, Iqbal, Muhammad N., Campbell, Ryan J., Maluleke, Matimba L., Moller, Carien, Nhlapo, Lerato, Maqungo, Sithombo, Flint, Margot, Nejthardt, Marcin B., Chetty, Sean, Naidoo, Rubendren, Guerra-Farfan, Ernesto, Tomas-Hernandez, Jordi, Garcia-Sanchez, Yaiza, Clua, Miriam Garrido, Molero-Garcia, Vicente, Minguell-Monyart, Joan, Teixidor-Serra, Jordi, Villar-Casares, Maria del Mar, Marsa, Jordi Selga, Porcel-Vazquez, Juan A., Andres-Peiro, Jose-Vicente, Aguilar, Marc, Mestre-Torres, Jaume, Colomina, Maria J., Guilabert, Patricia, Gozalo, M Luisa Paños, Abarca, Luis, Martin, Nuria, Usua, Gemma, Martinez-Ripol, Pedro, Posada, M.A. Gonzalez, Lalueza-Broto, Pilar, Sanchez-Raya, Judith, Camarena, Jorge Nuñez, Fraguas-Castany, Antoni, Balaguer-Castro, Mariano, Torner, Pere, Jornet-Gibert, Montsant, Serrano-Sanz, Jorge, Cámara-Cabrera, Jaume, Salomó-Domènech, Mònica, Yela-Verdú, Christian, Peig-Font, Anna, Ricol, Laura, Carreras-Castañer, Anna, Martínez-Sañudo, Luis, Herranz, Susana, Feijoo-Massó, Carlos, Sianes-Gallén, Mònica, Castillόn, Pablo, Bernaus, Martí, Quintas, Saioa, Gόmez, Olga, Salvador, Jordi, Abarca, Javier, Estrada, Cristina, Novellas, Marga, Torra, Mercè, Dealbert, Alfred, Macho, Oscar, Ivanov, Alexia, Valldosera, Esther, Arroyo, Marta, Pey, Borja, Yuste, Antoni, Mateo, Llorenç, Caso, Julio De, Anaya, Rafael, Higa-Sansone, J.L., Millan, Angelica, Baños, Victoria, Herrera-Mateo, Sergio, Aguado, Hector, Martinez-Municio, Gonzalo, León, Ricardo, Santiago-Maniega, Silvia, Zabalza, Ana, Labrador, Gregorio, Guerado, Enrique, Ramirez-Roldan, Alicia, Cruz, Encarnacion, Cano, Juan R, Bogallo, Jose M, Sangasoongsong, Paphon, Kulachote, Noratep, Sirisreetreerux, Norachart, Pengrung, Nachapan, Chalacheewa, Theerawat, Arnuntasupakul, Vanlapa, Yingchoncharoen, Teerapat, Naratreekoon, Bundit, Kadry, Miriam A, Thayaparan, Surendini, Abdlaziz, Ihab, Aframian, Arash, Imbuldeniya, Arjuna, Movahedi, Roya, Omran, Sherif, Vizcaychipi, Marcela P, Correia, Patricia, Patil, Shashank, Haire, Kevin, Mayor, Amy S.E., Dillingham, Sally, Nicholson, Laura, Elnaggar, Mohamed, John, Joby, Nanjayan, Shashi K, Parker, Martyn J, O'Sullivan, Susan, Marmor, Meir T, Matityahu, Amir, McClellan, Robert T, Comstock, Curt, Ding, Anthony, Toogood, Paul, Slobogean, Gerard, Joseph, Katherine, O'Toole, Robert, Sciadini, Marcus, Ryan, Scott P, Clark, Molly E, Cassidy, Charles, Balonov, Konstantin, Bergese, Sergio D, Phieffer, Laura S, Gonzalez-Zacarias, Alicia A, Marcantonio, Andrew J, Borges, Flavia K., Bhandari, Mohit, Nuñez, Jorge H., Castillon, Pablo, De Caso Rodriguez, Julio, Aguado, Hector J., Popova, Ekaterine, Tonelli, Ana Claudia, Balasubramanian, Kumar, Vincent, Jessica, Harvey, Valerie, Kocaqi, Etri, and Devereaux, P.J.

Published
2024
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7. Hate Speech Detection in Roman Urdu

Author

Khan, Moin, Shahzad, Khurram, and Malik, Kamran

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Hate speech is a specific type of controversial content that is widely legislated as a crime that must be identified and blocked. However, due to the sheer volume and velocity of the Twitter data stream, hate speech detection cannot be performed manually. To address this issue, several studies have been conducted for hate speech detection in European languages, whereas little attention has been paid to low-resource South Asian languages, making the social media vulnerable for millions of users. In particular, to the best of our knowledge, no study has been conducted for hate speech detection in Roman Urdu text, which is widely used in the sub-continent. In this study, we have scrapped more than 90,000 tweets and manually parsed them to identify 5,000 Roman Urdu tweets. Subsequently, we have employed an iterative approach to develop guidelines and used them for generating the Hate Speech Roman Urdu 2020 corpus. The tweets in the this corpus are classified at three levels: Neutral-Hostile, Simple-Complex, and Offensive-Hate speech. As another contribution, we have used five supervised learning techniques, including a deep learning technique, to evaluate and compare their effectiveness for hate speech detection. The results show that Logistic Regression outperformed all other techniques, including deep learning techniques for the two levels of classification, by achieved an F1 score of 0.906 for distinguishing between Neutral-Hostile tweets, and 0.756 for distinguishing between Offensive-Hate speech tweets., Comment: This is a pre-print of a contribution published in ACM Transactions on Asian and Low-Resource Language Information Processing. The final version is available online at the given journal link

Published
2021
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11. Screw-Based Extruder Design for 3D Printing of Food Using Food Layered Technology

Author

Khan, Moin, Haleem, Abid, Javaid, Mohd, Bahl, Shashi, Prakash, Chander, Singh, Rajesh, Bagha, Ashok Kumar, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Sharma, Rohit, editor, Kannojiya, Ravindra, editor, Garg, Naveen, editor, and Gautam, Sachin S., editor

Published
2023
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16. Sentiment Classification of Customer Reviews about Automobiles in Roman Urdu

Author

Khan, Moin and Malik, Kamran

Subjects
Computer Science - Computation and Language
Abstract

Text mining is a broad field having sentiment mining as its important constituent in which we try to deduce the behavior of people towards a specific item, merchandise, politics, sports, social media comments, review sites etc. Out of many issues in sentiment mining, analysis and classification, one major issue is that the reviews and comments can be in different languages like English, Arabic, Urdu etc. Handling each language according to its rules is a difficult task. A lot of research work has been done in English Language for sentiment analysis and classification but limited sentiment analysis work is being carried out on other regional languages like Arabic, Urdu and Hindi. In this paper, Waikato Environment for Knowledge Analysis (WEKA) is used as a platform to execute different classification models for text classification of Roman Urdu text. Reviews dataset has been scrapped from different automobiles sites. These extracted Roman Urdu reviews, containing 1000 positive and 1000 negative reviews, are then saved in WEKA attribute-relation file format (arff) as labeled examples. Training is done on 80% of this data and rest of it is used for testing purpose which is done using different models and results are analyzed in each case. The results show that Multinomial Naive Bayes outperformed Bagging, Deep Neural Network, Decision Tree, Random Forest, AdaBoost, k-NN and SVM Classifiers in terms of more accuracy, precision, recall and F-measure., Comment: This is a pre-print of a contribution published in Advances in Intelligent Systems and Computing (editors: Kohei Arai, Supriya Kapoor and Rahul Bhatia) published by Springer, Cham. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-03405-4_44

Published
2018
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21. Prophylaxis for preventing venous thromboembolism in knee arthroscopy and soft tissue reconstruction: consensus statements from an international panel of experts

Author

Easwaran, Raju, Khan, Moin, Sancheti, Parag, Shyam, Ashok, Bhandari, Mohit, Ranawat, Anil S., Thakkar, Savyasachi, Parikh, Shital, Musahl, Volker, Joglekar, Siddharth, J.Deshmukh, Ajit, Plancher, Kevin, Verma, Nikhil, McAllister, David, Verdonk, Peter, Lustig, Sebastien, Chandrateya, Amit, Smigleiski, Robert, Solayar, Gandhi, Chernchujit, Bancha, Yung, Patrick, Budhiparama, Nicolaas, Hoshino, Yuichi, White, Nathan, Parker, David, Clatworthy, Mark, Brown, Charlie, Manzary, Mojieb, Rajan, David, Narvekar, Abhay, Tapasvi, Sachin, Pardiwala, Dinshaw, Panigrahi, Ranjit, Arumugam, S., Kapoor, Vikash, Mody, Bharat, Maheshwari, Jitender, Dahiya, Vivek, Joseph, Clement, Laddha, Mukesh, and Rajgopal, Ashok

Published
2022
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26. Outcomes of patients undergoing anatomical total shoulder arthroplasty with augmented glenoid components – a systematic review.

Author

Prada, Carlos, Al-Mohrej, Omar A, Siddiqui, Salwa, and Khan, Moin

Subjects
SHOULDER osteoarthritis, REOPERATION, TOTAL shoulder replacement, TREATMENT effectiveness, ARTHROPLASTY, MEDLINE
Abstract

Background: Glenoid loosening is an issue in anatomic total shoulder arthroplasty (a-TSA). This has been attributed to abnormal glenoid anatomy, common among these patients. Different alternatives have been proposed to tackle glenoid bone loss and restore joint alignment with augmented glenoid implants being increasingly used to deal with this problem. This systematic review aims to evaluate the clinical and radiological outcomes of patients undergoing augmented glenoid a-TSAs. Our hypothesis was that augmented glenoid components will lead to good patient outcomes with a low incidence of complications and revision procedures. Methods: MEDLINE, EMBASE, CENTRAL and CINHAL were searched from inception to February 2022 for information pertaining to outcomes of patients undergoing a-TSA with augmented glenoid implants. Results: Eighteen studies reported on outcomes of 814 a-TSA (800 participants) with a mean follow-up of 3.7 years. Most studies (67%) were Type IV level of evidence. Almost 70% of participants underwent an a-TSA secondary to primary glenohumeral osteoarthritis. Most glenoids were type B2 (73%). Augmented glenoids material was mostly all-polyethylene (81%) with full wedge (45%) and stepped components (38%) designs being the most common. Most studies reported good clinical outcomes. 17 patients (4%) underwent a revision surgery. Conclusions: Our review found that patients undergoing a-TSA with augmented glenoid components report good outcomes at short-to-mid-term follow-up. Further research is warranted to determine if such outcomes remain similar in long term. Level of evidence: Level III, Systematic Review of Therapeutic Studies. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Cryo–Pneumatic Compression Results in a Significant Decrease in Opioid Consumption After Shoulder Surgery: A Multicenter Randomized Controlled Trial.

Author

Khan, Moin, Phillips, Steven A., Mathew, Paul, Venkateswaran, Vikram, Haverstock, John, Dagher, Danielle, Yardley, Darryl, Dick, David, and Bhandari, Mohit

Subjects
*SHOULDER surgery, *PATIENT selection, *PEARSON correlation (Statistics), *MORPHINE, *POSTOPERATIVE pain, *STATISTICAL sampling, *TREATMENT effectiveness, *RANDOMIZED controlled trials, *HEALTH surveys, *MANN Whitney U Test, *DESCRIPTIVE statistics, *COLD therapy, *OPIOID analgesics, *COMPRESSION garments, *QUALITY of life, *COMBINED modality therapy, *COMPRESSION therapy, *PAIN management, *HEALTH outcome assessment, *ADVERSE health care events
Abstract

Background: The management of pain after shoulder surgery typically includes the use of cryotherapy and the prescription of opioid analgesics. Much focus has been placed lately on the opioid epidemic, which in part is fueled by excessive prescription of opioid medication. Previous studies have found a combination of cryotherapy and compression effective at reducing analgesic consumption and increasing recovery in patients undergoing knee and spine surgery; however, efficacy in patients undergoing shoulder surgery has not been evaluated. Purpose: To evaluate the effectiveness of a cryo–pneumatic compression device on postoperative shoulder pain, narcotic use, and quality of life when compared with standard care cryotherapy. Study Design: Randomized controlled trial; Level of evidence, 2. Methods: In total, 200 patients older than 18 years scheduled for unilateral shoulder surgery were enrolled. Patients were randomized to receive either postoperative cryo–pneumatic compression or standard care. The intervention group received a cryo–pneumatic device, while the standard care group received the treating surgeon's preferred method of postoperative care, including standard cryotherapy. Narcotic use was evaluated by the number of oral morphine milligram equivalents consumed during the postoperative period, as well as the time to cessation of narcotic use. Patient-reported outcome measures consisted of a numeric rating scale pain score, 36-item Short Form Survey, patient experience assessed using the net promoter score, and adverse events. Outcomes were evaluated at 2, 6, and 12 weeks postoperatively. Results: Patients receiving cryo–pneumatic compression reported a significant decrease in opioid consumption when compared with standard care (oral morphine milligram equivalents median, 56.1 vs 112; P =.02468). A significant increase in self-reported function was seen in the cryo–pneumatic compression group at 2 weeks when compared with standard care (mean, 61.2 vs 54.2; P =.0412). Conclusion: In patients undergoing unilateral shoulder surgery, the use of cryotherapy with pneumatic compression, when compared with standard care, resulted in significantly decreased opioid consumption as well as increased function at 2 weeks. Registration: NCT04185064 (ClinicalTrials.gov identifier). [ABSTRACT FROM AUTHOR]

Published
2024
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28. Anterior Shoulder Instability Part II—Latarjet, Remplissage, and Glenoid Bone-Grafting—An International Consensus Statement

Author

Alaia, Michael J., Arciero, Robert A., Bedi, Asheesh, Brophy, Robert H., Calvo, Emilio, Campbell, Kirk A., Carter, Cordelia W., Cassidy, J Tristan, Ciccotti, Michael G., Cole, Brian J., Collin, Philippe, Cordasco, Frank A., Edwards, Sara E., Erickson, Brandon J., Favard, Luc, Frank, Rachel M., Funk, Lennard, Garrigues, Grant E., Di Giacomo, Giovanni, Gonzalez-Lomas, Guillem, Heuberer, Philipp R., Imhoff, Andreas B., Kelly, John D., Khan, Moin, Krych, Aaron J., Kuhn, John E., Kwon, Young M., Lädermann, Alexandre, Levine, William N., Fat, Darren Lim, Mazzocca, Augustus D., MacDonald, Peter B., McCarty, Eric C., Meislin, Robert J., Millett, Peter J., Molony, Diarmuid C., Moran, Cathal J., Moroder, Philipp, Moya, Daniel, O’Shea, Kieran, Owens, Brett D., Provencher, Matthew T., Rhee, Yong Girl, Rodeo, Scott A., Rokito, Andrew S., Rosso, Claudio, Scheibel, Markus, Verma, Nikhil N., Virk, Mandeep S., Walch, Gilles, Warren, Russell F., Waterman, Brian R., Whelan, Daniel B., Zuckerman, Joseph D., Hurley, Eoghan T., Matache, Bogdan A., Wong, Ivan, Itoi, Eiji, Strauss, Eric J., Delaney, Ruth A., Neyton, Lionel, Athwal, George S., Pauzenberger, Leo, Mullett, Hannan, and Jazrawi, Laith M.

Published
2022
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29. Anterior Shoulder Instability Part I—Diagnosis, Nonoperative Management, and Bankart Repair—An International Consensus Statement

Author

Alaia, Michael J., Arciero, Robert A., Bedi, Asheesh, Brophy, Robert H., Calvo, Emilio, Campbell, Kirk A., Carter, Cordelia W., Cassidy, J Tristan, Ciccotti, Michael G., Cole, Brian J., Collin, Philippe, Cordasco, Frank A., Edwards, Sara E., Erickson, Brandon J., Favard, Luc, Frank, Rachel M., Funk, Lennard, Garrigues, Grant E., Di Giacomo, Giovanni, Gonzalez-Lomas, Guillem, Heuberer, Philipp R., Imhoff, Andreas B., Kelly, John D., Khan, Moin, Krych, Aaron J., Kuhn, John E., Kwon, Young M., Lädermann, Alexandre, Levine, William N., Fat, Darren Lim, Mazzocca, Augustus D., MacDonald, Peter B., McCarty, Eric C., Meislin, Robert J., Millett, Peter J., Molony, Diarmuid C., Moran, Cathal J., Moroder, Philipp, Moya, Daniel, O’Shea, Kieran, Owens, Brett D., Provencher, Matthew T., Rhee, Yong Girl, Rodeo, Scott A., Rokito, Andrew S., Rosso, Claudio, Scheibel, Markus, Verma, Nikhil N., Virk, Mandeep S., Walch, Gilles, Warren, Russell F., Waterman, Brian R., Whelan, Daniel B., Zuckerman, Joseph D., Hurley, Eoghan T., Matache, Bogdan A., Wong, Ivan, Itoi, Eiji, Strauss, Eric J., Delaney, Ruth A., Neyton, Lionel, Athwal, George S., Pauzenberger, Leo, Mullett, Hannan, and Jazrawi, Laith M.

Published
2022
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30. Anterior Shoulder Instability Part III—Revision Surgery, Rehabilitation and Return to Play, and Clinical Follow-Up—An International Consensus Statement

Author

Alaia, Michael J., Arciero, Robert A., Bedi, Asheesh, Brophy, Robert H., Calvo, Emilio, Campbell, Kirk A., Carter, Cordelia W., Cassidy, J Tristan, Ciccotti, Michael G., Cole, Brian J., Collin, Philippe, Cordasco, Frank A., Edwards, Sara E., Erickson, Brandon J., Favard, Luc, Frank, Rachel M., Funk, Lennard, Garrigues, Grant E., Di Giacomo, Giovanni, Gonzalez-Lomas, Guillem, Heuberer, Philipp R., Imhoff, Andreas B., Kelly, John D., Khan, Moin, Krych, Aaron J., Kuhn, John E., Kwon, Young M., Lädermann, Alexandre, Levine, William N., Fat, Darren Lim, Mazzocca, Augustus D., MacDonald, Peter B., McCarty, Eric C., Meislin, Robert J., Millett, Peter J., Molony, Diarmuid C., Moran, Cathal J., Moroder, Philipp, Moya, Daniel, O’Shea, Kieran, Owens, Brett D., Provencher, Matthew T., Rhee, Yong Girl, Rodeo, Scott A., Rokito, Andrew S., Rosso, Claudio, Scheibel, Markus, Verma, Nikhil N., Virk, Mandeep S., Walch, Gilles, Warren, Russell F., Waterman, Brian R., Whelan, Daniel B., Zuckerman, Joseph D., Matache, Bogdan A., Hurley, Eoghan T., Wong, Ivan, Itoi, Eiji, Strauss, Eric J., Delaney, Ruth A., Neyton, Lionel, Athwal, George S., Pauzenberger, Leo, Mullett, Hannan, and Jazrawi, Laith M.

Published
2022
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43. The Continuous Fragility Index of Statistically Significant Findings in Randomized Controlled Trials That Compare Interventions for Anterior Shoulder Instability.

Author

Al-Asadi, Mohammed, Sherren, Michelle, Abdel Khalik, Hassaan, Leroux, Timothy, Ayeni, Olufemi R., Madden, Kim, and Khan, Moin

Subjects
SHOULDER joint surgery, MEDICAL information storage & retrieval systems, CONTINUING education units, DATA analysis, T-test (Statistics), SHOULDER joint, DECISION making in clinical medicine, RANDOMIZED controlled trials, DESCRIPTIVE statistics, META-analysis, MANN Whitney U Test, MEDLINE, SYSTEMATIC reviews, MEDICAL research, STATISTICS, CONFIDENCE intervals, COMPARATIVE studies, JOINT instability, REGRESSION analysis
Abstract

Background: Evidence-based care relies on robust research. The fragility index (FI) is used to assess the robustness of statistically significant findings in randomized controlled trials (RCTs). While the traditional FI is limited to dichotomous outcomes, a novel tool, the continuous fragility index (CFI), allows for the assessment of the robustness of continuous outcomes. Purpose: To calculate the CFI of statistically significant continuous outcomes in RCTs evaluating interventions for managing anterior shoulder instability (ASI). Study Design: Meta-analysis; Level of evidence, 2. Methods: A search was conducted across the MEDLINE, Embase, and CENTRAL databases for RCTs assessing management strategies for ASI from inception to October 6, 2022. Studies that reported a statistically significant difference between study groups in ≥1 continuous outcome were included. The CFI was calculated and applied to all available RCTs reporting interventions for ASI. Multivariable linear regression was performed between the CFI and various study characteristics as predictors. Results: There were 27 RCTs, with a total of 1846 shoulders, included. The median sample size was 61 shoulders (IQR, 43). The median CFI across 27 RCTs was 8.2 (IQR, 17.2; 95% CI, 3.6-15.4). The median CFI was 7.9 (IQR, 21; 95% CI, 1-22) for 11 studies comparing surgical methods, 22.6 (IQR, 16; 95% CI, 8.2-30.4) for 6 studies comparing nonsurgical reduction interventions, 2.8 for 3 studies comparing immobilization methods, and 2.4 for 3 studies comparing surgical versus nonsurgical interventions. Significantly, 22 of 57 included outcomes (38.6%) from studies with completed follow-up data had a loss to follow-up exceeding their CFI. Multivariable regression demonstrated that there was a statistically significant positive correlation between a trial's sample size and the CFI of its outcomes (r = 0.23 [95% CI, 0.13-0.33]; P <.001). Conclusion: More than a third of continuous outcomes in ASI trials had a CFI less than the reported loss to follow-up. This carries the significant risk of reversing trial findings and should be considered when evaluating available RCT data. We recommend including the FI, CFI, and loss to follow-up in the abstracts of future RCTs. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Sentiment Classification of Customer’s Reviews About Automobiles in Roman Urdu

Author

Khan, Moin, Malik, Kamran, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Arai, Kohei, editor, Kapoor, Supriya, editor, and Bhatia, Rahul, editor

Published
2019
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