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4. Can't Inflate Data? Let the Models Unite and Vote: Data-Agnostic Method to Avoid Overfit with Small Data

Author

Shimmei, Machi and Matsuda, Noboru

Abstract

We propose an innovative, effective, and data-agnostic method to train a deep-neural network model with an extremely small training dataset, called VELR (Voting-based Ensemble Learning with Rejection). In educational research and practice, providing valid labels for a sufficient amount of data to be used for supervised learning can be very costly and often impractical. The shortage of training data often results in deep neural networks being overfitting. There are many methods to avoid overfitting such as data augmentation and regularization. Though, data augmentation is considerably data dependent and does not usually work well for natural language processing tasks. Moreover, regularization is often quite task specific and costly. To address this issue, we propose an ensemble of overfitting models with uncertainty-based rejection. We hypothesize that misclassification can be identified by estimating the distribution of the class-posterior probability P(y|x) as a random variable. The proposed VELR method is data independent, and it does not require changes to the model structure or the re-training of the model. Empirical studies demonstrated that VELR achieved classification accuracy of 0.7 with only 200 samples per class on the CIFAR-10 dataset, but 75% of input samples were rejected. VELR was also applied to a question generation task using a BERT language model with only 350 training data points, which resulted in generating questions that are indistinguishable from human-generated questions. The paper concludes that VELR has potential applications to a broad range of real-world problems where misclassification is very costly, which is quite common in the educational domain. [For the complete proceedings, see ED630829.]

Published
2023

5. Latent Skill Mining and Labeling from Courseware Content

Author

Matsuda, Noboru, Wood, Jesse, Shrivastava, Raj, Shimmei, Machi, and Bier, Norman

Abstract

A model that maps the requisite skills, or knowledge components, to the contents of an online course is necessary to implement many adaptive learning technologies. However, developing a skill model and tagging courseware contents with individual skills can be expensive and error prone. We propose a technology to automatically identify latent skills from instructional text on existing online courseware called SMART (Skill Model mining with Automated detection of Resemblance among Texts). SMART is capable of mining, labeling, and mapping skills without using an existing skill model or student learning (aka response) data. The goal of our proposed approach is to mine latent skills from assessment items included in existing courseware, provide discovered skills with human-friendly labels, and map didactic paragraph texts with skills. This way, mapping between assessment items and paragraph texts is formed. In doing so, automated skill models produced by SMART will reduce the workload of courseware developers while enabling adaptive online content at the launch of the course. In our evaluation study, we applied SMART to two existing authentic online courses. We then compared machine-generated skill models and human-crafted skill models in terms of the accuracy of predicting students' learning. We also evaluated the similarity between machine-generated and human-crafted skill models. The results show that student models based on SMART-generated skill models were equally predictive of students' learning as those based on human-crafted skill models--as validated on two OLI (Open Learning Initiative) courses. Also, SMART can generate skill models that are highly similar to human-crafted models as evidenced by the normalized mutual information (NMI) values.

Published
2022

8. Migrant-Oriented Japanese Language Programs in Tokyo

Author

Seunghun J. Lee and Machi Niiya

Abstract

Language proficiency is identified as one of the most important factors for successful migrant integration. Japan has had a sizeable increase of migrants in the past few years, and these migrants needed to build their Japanese proficiency. Focusing on municipalities in the 23 Tokyo wards, we surveyed what types of Japanese as a Second Language (JSL) programs are available, and how this information is disseminated to the public. It was encouraging that the central government and the government agencies of Tokyo have created language policies and produced handbooks for JSL teachers. All except one ward in Tokyo offer information about JSL programs in their respective ward, and many programs were run by the municipalities or by volunteer groups, suggesting an institutionalized move for supporting language education for migrants. We also identified two points that need to be considered for a better language education system for migrants: (a) a need for a certification program for JSL teachers and (b) a need for developing a language proficiency test that does not assume studying in an academic program.

Published
2021

9. Learning a Policy Primes Quality Control: Towards Evidence-Based Automation of Learning Engineering

Author

Shimmei, Machi and Matsuda, Noboru

Abstract

One of the most challenging issues for online courseware engineering is to maintain the quality of instructional components, such as written text, video, and assessments. Learning engineers would like to know how individual instructional components contributed to students' learning. However, it is a hard task because it requires significant expertise in learning science, learning technology, and subject matter pedagogy. To address this challenge, we propose an innovative application of reinforcement learning (RL) as an assessor of instructional components implemented in given online courseware. After students activities are converted into Markov decision process (MDP), a collection of actions (each corresponds to an instructional component) suggested as a policy is analyzed. As a consequence, the usefulness of individual actions with regards to achieving ideal learning outcomes will be suggested. The proposed RL application is invented for human-in-the-loop learning engineering method called RAFINE. In the RAFINE framework, a machine generates a list of the least contributing instructional components on the given online courseware by interpreting the whole policy. The courseware developers modify those suggested components. As a proof of concept, this paper describes an evaluation study where online learning was simulated on hypothetical online courseware. The results showed that over 90% of ineffective instructional components were correctly identified as ineffective on average. [For the full proceedings, see ED607784.]

Published
2020

12. Evaluation of the US COVID-19 Scenario Modeling Hub for informing pandemic response under uncertainty

Author

Howerton, Emily, Contamin, Lucie, Mullany, Luke C., Qin, Michelle, Reich, Nicholas G., Bents, Samantha, Borchering, Rebecca K., Jung, Sung-mok, Loo, Sara L., Smith, Claire P., Levander, John, Kerr, Jessica, Espino, J., van Panhuis, Willem G., Hochheiser, Harry, Galanti, Marta, Yamana, Teresa, Pei, Sen, Shaman, Jeffrey, Rainwater-Lovett, Kaitlin, Kinsey, Matt, Tallaksen, Kate, Wilson, Shelby, Shin, Lauren, Lemaitre, Joseph C., Kaminsky, Joshua, Hulse, Juan Dent, Lee, Elizabeth C., McKee, Clifton D., Hill, Alison, Karlen, Dean, Chinazzi, Matteo, Davis, Jessica T., Mu, Kunpeng, Xiong, Xinyue, Pastore y Piontti, Ana, Vespignani, Alessandro, Rosenstrom, Erik T., Ivy, Julie S., Mayorga, Maria E., Swann, Julie L., España, Guido, Cavany, Sean, Moore, Sean, Perkins, Alex, Hladish, Thomas, Pillai, Alexander, Ben Toh, Kok, Longini, Jr., Ira, Chen, Shi, Paul, Rajib, Janies, Daniel, Thill, Jean-Claude, Bouchnita, Anass, Bi, Kaiming, Lachmann, Michael, Fox, Spencer J., Meyers, Lauren Ancel, Srivastava, Ajitesh, Porebski, Przemyslaw, Venkatramanan, Srini, Adiga, Aniruddha, Lewis, Bryan, Klahn, Brian, Outten, Joseph, Hurt, Benjamin, Chen, Jiangzhuo, Mortveit, Henning, Wilson, Amanda, Marathe, Madhav, Hoops, Stefan, Bhattacharya, Parantapa, Machi, Dustin, Cadwell, Betsy L., Healy, Jessica M., Slayton, Rachel B., Johansson, Michael A., Biggerstaff, Matthew, Truelove, Shaun, Runge, Michael C., Shea, Katriona, Viboud, Cécile, and Lessler, Justin

Published
2023
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17. 'Life Is Based on Reciprocity, so Be Generous': Ethical Work in Doctoral Acknowledgements

Author

Grant, Barbara M., Sato, Machi, and Skelling, Jules

Abstract

Purpose: This paper aims to explore doctoral candidates' ethical work in writing the acknowledgements section of their theses. With interest in the formation of academic identities/subjectivities, the authors explore acknowledgements writing as always potentially a form of parrhesia or risky truth-telling, through which the candidate places themselves in their relations to others rather than in their claims to knowledge (Luxon, 2008). Design/methodology/approach: Doctoral candidates from all faculties in one Japanese and one Aotearoa New Zealand university participated in focus groups where they discussed the genre of thesis acknowledgements, drafted their own version and wrote a reflective commentary/backstory. Findings: Viewing the backstories through the lens of parrhesia (with its entangled matters of frankness, truth, risk, criticism and duty) showed candidates engaged in complex ethical decision-making processes with, at best, "ambiguous ethical resources" (Luxon, 2008, p. 381) arising from their academic and personal lives. Candidates used these resources to try and position themselves as both properly academic and more than academic -- as knowing selves and relational selves. Originality/value: This study bares the ethical riskiness of writing doctoral acknowledgements, as doctoral candidates navigate the tensions between situating themselves "truthfully" in their relations with others while striking the necessary pose of intellectual independence (originality). In a context where there is evidence that examiners not only read acknowledgements to ascertain independence, student and/or supervisor quality and the "human being behind the thesis" (Kumar and Sanderson, 2020, p. 285) but also show bias in those readings, this study advises reader caution about drawing inferences from acknowledgements texts. They are not simply transparent. As examiners and other readers make sense, judgments even, of these tiny, often fascinating, glimpses into a candidate's doctoral experience, they need to understand that a host of unpredictable tensions with myriad ambiguous effects are present on the page.

Published
2022
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21. Prevalence of common aneuploidy in twin pregnancies

Author

Konishi, Akiko, Samura, Osamu, Muromoto, Jin, Okamoto, Yoko, Takahashi, Hironori, Kasai, Yasuyo, Ichikawa, Mayuko, Yamada, Naoki, Kato, Noriko, Sato, Hiroshi, Hamada, Hiromi, Nakanami, Naoyuki, Machi, Maya, Ichizuka, Kiyotake, Sunami, Rei, Tanaka, Toshitaka, Yonetani, Naoto, Kamei, Yoshimasa, Nagamatsu, Takeshi, Matsumoto, Mariko, Tairaku, Shinya, Fujiwara, Arisa, Nakamura, Hiroaki, Harada, Takashi, Watanabe, Takafumi, Sasaki, Shoko, Kawaguchi, Satoshi, Minami, Sawako, Ogawa, Masaki, Miura, Kiyonori, Suzumori, Nobuhiro, Kojima, Junya, Kotani, Tomomi, Sasaki, Rumi, Baba, Tsukasa, Toyofuku, Aya, Endo, Masayuki, Takeshita, Naoki, Taketani, Takeshi, Sase, Masakatsu, Matsubara, Keiichi, Hayata, Kei, Hamada, Yoshinobu, Egawa, Makiko, Kakinuma, Toshiyuki, Matsushima, Sachio, Kitagawa, Michihiro, Shiga, Tomomi, Kurashina, Ryuhei, Hamada, Hironori, Takagi, Hiroaki, Kondo, Akane, Miharu, Norio, Yamashita, Michiko, Horiya, Madoka, Morimoto, Keiji, Takahashi, Ken, Okamoto, Aikou, Sekizawa, Akihiko, and Sago, Haruhiko

Published
2022
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37. Improving U.S. Competitiveness with K-12 STEM Education and Training. Heritage Special Report. SR-57. A Report on the STEM Education and National Security Conference, October 21-23, 2008

Author

Heritage Foundation and Machi, Ethel

Abstract

From October 21 to 23, 2008, The Heritage Foundation, supported by the McCormick Tribune Foundation, convened leading education and national security experts as well as private-sector representatives to discuss methods for strengthening America's competitiveness by improving its performance in science, technology, engineering, and mathematics (STEM) education. This report attempts to describe the current state of STEM education, summarize the main points discussed at the conference, and present the conference attendees' conclusions on how to improve STEM education and advance America's defense capabilities and economy. This report is aimed at education and private-sector leaders as well as at national defense strategists, but can also be useful to other interested parties. The private sector and defense industry will need to work closely with education reformers to create a feasible plan for improving the current state of STEM education: Success in both industry and defense is vitally linked and inextricably tied to the capabilities of the STEM workforce. The report makes the following recommendations to federal and state policymakers and to the private sector: (1) Strengthen the quality of the elementary and secondary teacher workforce, particularly in STEM subjects; (2) Reform the traditional public school system to encourage greater innovation and superior instruction; (3) Implement aggressive reforms to change the governance of the traditional public school system; and (4) Resolve the H-1B visa shortage.

Published
2009

39. The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020)

Author

Egi, Moritoki, Ogura, Hiroshi, Yatabe, Tomoaki, Atagi, Kazuaki, Inoue, Shigeaki, Iba, Toshiaki, Kakihana, Yasuyuki, Kawasaki, Tatsuya, Kushimoto, Shigeki, Kuroda, Yasuhiro, Kotani, Joji, Shime, Nobuaki, Taniguchi, Takumi, Tsuruta, Ryosuke, Doi, Kent, Doi, Matsuyuki, Nakada, Taka-aki, Nakane, Masaki, Fujishima, Seitaro, Hosokawa, Naoto, Masuda, Yoshiki, Matsushima, Asako, Matsuda, Naoyuki, Yamakawa, Kazuma, Hara, Yoshitaka, Sakuraya, Masaaki, Ohshimo, Shinichiro, Aoki, Yoshitaka, Inada, Mai, Umemura, Yutaka, Kawai, Yusuke, Kondo, Yutaka, Saito, Hiroki, Taito, Shunsuke, Takeda, Chikashi, Terayama, Takero, Tohira, Hideo, Hashimoto, Hideki, Hayashida, Kei, Hifumi, Toru, Hirose, Tomoya, Fukuda, Tatsuma, Fujii, Tomoko, Miura, Shinya, Yasuda, Hideto, Abe, Toshikazu, Andoh, Kohkichi, Iida, Yuki, Ishihara, Tadashi, Ide, Kentaro, Ito, Kenta, Ito, Yusuke, Inata, Yu, Utsunomiya, Akemi, Unoki, Takeshi, Endo, Koji, Ouchi, Akira, Ozaki, Masayuki, Ono, Satoshi, Katsura, Morihiro, Kawaguchi, Atsushi, Kawamura, Yusuke, Kudo, Daisuke, Kubo, Kenji, Kurahashi, Kiyoyasu, Sakuramoto, Hideaki, Shimoyama, Akira, Suzuki, Takeshi, Sekine, Shusuke, Sekino, Motohiro, Takahashi, Nozomi, Takahashi, Sei, Takahashi, Hiroshi, Tagami, Takashi, Tajima, Goro, Tatsumi, Hiroomi, Tani, Masanori, Tsuchiya, Asuka, Tsutsumi, Yusuke, Naito, Takaki, Nagae, Masaharu, Nagasawa, Ichiro, Nakamura, Kensuke, Nishimura, Tetsuro, Nunomiya, Shin, Norisue, Yasuhiro, Hashimoto, Satoru, Hasegawa, Daisuke, Hatakeyama, Junji, Hara, Naoki, Higashibeppu, Naoki, Furushima, Nana, Furusono, Hirotaka, Matsuishi, Yujiro, Matsuyama, Tasuku, Minematsu, Yusuke, Miyashita, Ryoichi, Miyatake, Yuji, Moriyasu, Megumi, Yamada, Toru, Yamada, Hiroyuki, Yamamoto, Ryo, Yoshida, Takeshi, Yoshida, Yuhei, Yoshimura, Jumpei, Yotsumoto, Ryuichi, Yonekura, Hiroshi, Wada, Takeshi, Watanabe, Eizo, Aoki, Makoto, Asai, Hideki, Abe, Takakuni, Igarashi, Yutaka, Iguchi, Naoya, Ishikawa, Masami, Ishimaru, Go, Isokawa, Shutaro, Itakura, Ryuta, Imahase, Hisashi, Imura, Haruki, Irinoda, Takashi, Uehara, Kenji, Ushio, Noritaka, Umegaki, Takeshi, Egawa, Yuko, Enomoto, Yuki, Ota, Kohei, Ohchi, Yoshifumi, Ohno, Takanori, Ohbe, Hiroyuki, Oka, Kazuyuki, Okada, Nobunaga, Okada, Yohei, Okano, Hiromu, Okamoto, Jun, Okuda, Hiroshi, Ogura, Takayuki, Onodera, Yu, Oyama, Yuhta, Kainuma, Motoshi, Kako, Eisuke, Kashiura, Masahiro, Kato, Hiromi, Kanaya, Akihiro, Kaneko, Tadashi, Kanehata, Keita, Kano, Ken-ichi, Kawano, Hiroyuki, Kikutani, Kazuya, Kikuchi, Hitoshi, Kido, Takahiro, Kimura, Sho, Koami, Hiroyuki, Kobashi, Daisuke, Saiki, Iwao, Sakai, Masahito, Sakamoto, Ayaka, Sato, Tetsuya, Shiga, Yasuhiro, Shimoto, Manabu, Shimoyama, Shinya, Shoko, Tomohisa, Sugawara, Yoh, Sugita, Atsunori, Suzuki, Satoshi, Suzuki, Yuji, Suhara, Tomohiro, Sonota, Kenji, Takauji, Shuhei, Takashima, Kohei, Takahashi, Sho, Takahashi, Yoko, Takeshita, Jun, Tanaka, Yuuki, Tampo, Akihito, Tsunoyama, Taichiro, Tetsuhara, Kenichi, Tokunaga, Kentaro, Tomioka, Yoshihiro, Tomita, Kentaro, Tominaga, Naoki, Toyosaki, Mitsunobu, Toyoda, Yukitoshi, Naito, Hiromichi, Nagata, Isao, Nagato, Tadashi, Nakamura, Yoshimi, Nakamori, Yuki, Nahara, Isao, Naraba, Hiromu, Narita, Chihiro, Nishioka, Norihiro, Nishimura, Tomoya, Nishiyama, Kei, Nomura, Tomohisa, Haga, Taiki, Hagiwara, Yoshihiro, Hashimoto, Katsuhiko, Hatachi, Takeshi, Hamasaki, Toshiaki, Hayashi, Takuya, Hayashi, Minoru, Hayamizu, Atsuki, Haraguchi, Go, Hirano, Yohei, Fujii, Ryo, Fujita, Motoki, Fujimura, Naoyuki, Funakoshi, Hiraku, Horiguchi, Masahito, Maki, Jun, Masunaga, Naohisa, Matsumura, Yosuke, Mayumi, Takuya, Minami, Keisuke, Miyazaki, Yuya, Miyamoto, Kazuyuki, Murata, Teppei, Yanai, Machi, Yano, Takao, Yamada, Kohei, Yamada, Naoki, Yamamoto, Tomonori, Yoshihiro, Shodai, Tanaka, Hiroshi, and Nishida, Osamu

Published
2021
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48. Penumbral imaging and functional outcome in patients with anterior circulation ischaemic stroke treated with endovascular thrombectomy versus medical therapy: a meta-analysis of individual patient-level data

Author

Berkhemer, Olvert A, Fransen, Puck SS, Beumer, Debbie, van den Berg, Lucie A, Lingsma, Hester F, Yoo, Albert J, Schonewille, Wouter J, Vos, Jan Albert, Nederkoorn, Paul J, Wermer, Marieke JH, van Walderveen, Marianne AA, Staals, Julie, Hofmeijer, Jeannette, van Oostayen, Jacques A., Lycklama à Nijeholt, Geert J., Boiten, Jelis, Brouwer, Patrick A., Emmer, Bart J., de Bruijn, Sebastiaan F., van Dijk, Lukas C., Kappelle, Jaap, Lo, Rob H, van Dijk, Ewoud J., de Vries, Joost, de Kort, Paul L.M., van Rooij, Willem Jan J., van den Berg, Jan S.P., van Hasselt, Boudewijn A.A.M., Aerden, Leo A.M., Dallinga, René J., Visser, Marieke C., Bot, Joseph C.J., Vroomen, Patrick C., Eshghi, Omid, Schreuder, Tobien H.C.M.L., Heijboer, Roel J.J., Keizer, Koos, Tielbeek, Alexander V., den Hertog, Heleen M., Gerrits, Dick G., van den Berg-Vos, Renske M., Karas, Giorgos B., Steyerberg, Ewout W., Flach, Zwenneke, Marquering, Henk A., Sprengers, Marieke E.S., Jenniskens, Sjoerd F.M., Beenen, Ludo F.M., van den Berg, René, Koudstaal, Peter J., van Zwam, Wim H., Roos, Yvo B.W.E.M., van der Lugt, Aad, van Oostenbrugge, Robert J., Majoie, Charles B.L.M., Dippel, Diederik W.J., Brown, Martin M., Liebig, Thomas, Stijnen, Theo, Andersson, Tommy, Mattle, Heinrich, Wahlgren, Nils, van der Heijden, Esther, Ghannouti, Naziha, Fleitour, Nadine, Hooijenga, Imke, Puppels, Corina, Pellikaan, Wilma, Geerling, Annet, Lindl-Velema, Annemieke, van Vemde, Gina, de Ridder, Ans, Greebe, Paut, de Bont-Stikkelbroeck, José, de Meris, Joke, Janssen, Kirsten, Struijk, Willy, Licher, Silvan, Boodt, Nikki, Ros, Adriaan, Venema, Esmee, Slokkers, Ilse, Ganpat, Raymie-Jayce, Mulder, Maxim, Saiedie, Nawid, Heshmatollah, Alis, Schipperen, Stefanie, Vinken, Stefan, van Boxtel, Tiemen, Koets, Jeroen, Boers, Merel, Santos, Emilie, Borst, Jordi, Jansen, Ivo, Kappelhof, Manon, Lucas, Marit, Geuskens, Ralph, Barros, Renan Sales, Dobbe, Roeland, Csizmadia, Marloes, Hill, MD, Goyal, M, Demchuk, AM, Menon, BK, Eesa, M, Ryckborst, KJ, Wright, MR, Kamal, NR, Andersen, L, Randhawa, PA, Stewart, T, Patil, S, Minhas, P, Almekhlafi, M, Mishra, S, Clement, F, Sajobi, T, Shuaib, A, Montanera, WJ, Roy, D, Silver, FL, Jovin, TG, Frei, DF, Sapkota, B, Rempel, JL, Thornton, J, Williams, D, Tampieri, D, Poppe, AY, Dowlatshahi, D, Wong, JH, Mitha, AP, Subramaniam, S, Hull, G, Lowerison, MW, Salluzzi, M, Maxwell, M, Lacusta, S, Drupals, E, Armitage, K, Barber, PA, Smith, EE, Morrish, WF, Coutts, SB, Derdeyn, C, Demaerschalk, B, Yavagal, D, Martin, R, Brant, R, Yu, Y, Willinsky, RA, Weill, A, Kenney, C, Aram, H, Stys, PK, Watson, TW, Klein, G, Pearson, D, Couillard, P, Trivedi, A, Singh, D, Klourfeld, E, Imoukhuede, O, Nikneshan, D, Blayney, S, Reddy, R, Choi, P, Horton, M, Musuka, T, Dubuc, V, Field, TS, Desai, J, Adatia, S, Alseraya, A, Nambiar, V, van Dijk, R, Newcommon, NJ, Schwindt, B, Butcher, KS, Jeerakathil, T, Buck, B, Khan, K, Naik, SS, Emery, DJ, Owen, RJ, Kotylak, TB, Ashforth, RA, Yeo, TA, McNally, D, Siddiqui, M, Saqqur, M, Hussain, D, Kalashyan, H, Manosalva, A, Kate, M, Gioia, L, Hasan, S, Mohammad, A, Muratoglu, M, Cullen, A, Brennan, P, O'Hare, A, Looby, S, Hyland, D, Duff, S, McCusker, M, Hallinan, B, Lee, S, McCormack, J, Moore, A, O'Connor, M, Donegan, C, Brewer, L, Martin, A, Murphy, S, O'Rourke, K, Smyth, S, Kelly, P, Lynch, T, Daly, T, O'Brien, P, O'Driscoll, A, Martin, M, Collins, R, Coughlan, T, McCabe, D, O'Neill, D, Mulroy, M, Lynch, O, Walsh, T, O'Donnell, M, Galvin, T, Harbison, J, McElwaine, P, Mulpeter, K, McLoughlin, C, Reardon, M, Harkin, E, Dolan, E, Watts, M, Cunningham, N, Fallon, C, Gallagher, S, Cotter, P, Crowe, M, Doyle, R, Noone, I, Lapierre, M, Coté, VA, Lanthier, S, Odier, C, Durocher, A, Raymond, J, Daneault, N, Deschaintre, Y, Jankowitz, B, Baxendell, L, Massaro, L, Jackson-Graves, C, Decesare, S, Porter, P, Armbruster, K, Adams, A, Billigan, J, Oakley, J, Ducruet, A, Jadhav, A, Giurgiutiu, D-V, Aghaebrahim, A, Reddy, V, Hammer, M, Starr, M, Totoraitis, V, Wechsler, L, Streib, S, Rangaraju, S, Campbell, D, Rocha, M, Gulati, D, Krings, T, Kalman, L, Cayley, A, Williams, J, Wiegner, R, Casaubon, LK, Jaigobin, C, del Campo, JM, Elamin, E, Schaafsma, JD, Agid, R, Farb, R, ter Brugge, K, Sapkoda, BL, Baxter, BW, Barton, K, Knox, A, Porter, A, Sirelkhatim, A, Devlin, T, Dellinger, C, Pitiyanuvath, N, Patterson, J, Nichols, J, Quarfordt, S, Calvert, J, Hawk, H, Fanale, C, Bitner, A, Novak, A, Huddle, D, Bellon, R, Loy, D, Wagner, J, Chang, I, Lampe, E, Spencer, B, Pratt, R, Bartt, R, Shine, S, Dooley, G, Nguyen, T, Whaley, M, McCarthy, K, Teitelbaum, J, Poon, W, Campbell, N, Cortes, M, Lum, C, Shamloul, R, Robert, S, Stotts, G, Shamy, M, Steffenhagen, N, Blacquiere, D, Hogan, M, AlHazzaa, M, Basir, G, Lesiuk, H, Iancu, D, Santos, M, Choe, H, Weisman, DC, Jonczak, K, Blue-Schaller, A, Shah, Q, MacKenzie, L, Klein, B, Kulandaivel, K, Kozak, O, Gzesh, DJ, Harris, LJ, Khoury, JS, Mandzia, J, Pelz, D, Crann, S, Fleming, L, Hesser, K, Beauchamp, B, Amato-Marzialli, B, Boulton, M, Lopez- Ojeda, P, Sharma, M, Lownie, S, Chan, R, Swartz, R, Howard, P, Golob, D, Gladstone, D, Boyle, K, Boulos, M, Hopyan, J, Yang, V, Da Costa, L, Holmstedt, CA, Turk, AS, Navarro, R, Jauch, E, Ozark, S, Turner, R, Phillips, S, Shankar, J, Jarrett, J, Gubitz, G, Maloney, W, Vandorpe, R, Schmidt, M, Heidenreich, J, Hunter, G, Kelly, M, Whelan, R, Peeling, L, Burns, PA, Hunter, A, Wiggam, I, Kerr, E, Watt, M, Fulton, A, Gordon, P, Rennie, I, Flynn, P, Smyth, G, O'Leary, S, Gentile, N, Linares, G, McNelis, P, Erkmen, K, Katz, P, Azizi, A, Weaver, M, Jungreis, C, Faro, S, Shah, P, Reimer, H, Kalugdan, V, Saposnik, G, Bharatha, A, Li, Y, Kostyrko, P, Marotta, T, Montanera, W, Sarma, D, Selchen, D, Spears, J, Heo, JH, Jeong, K, Kim, DJ, Kim, BM, Kim, YD, Song, D, Lee, K-J, Yoo, J, Bang, OY, Rho, S, Lee, J, Jeon, P, Kim, KH, Cha, J, Kim, SJ, Ryoo, S, Lee, MJ, Sohn, S-I, Kim, C-H, Ryu, H-G, Hong, J-H, Chang, H-W, Lee, C-Y, Rha, J, Davis, Stephen M, Donnan, Geoffrey A, Campbell, Bruce CV, Mitchell, Peter J, Churilov, Leonid, Yan, Bernard, Dowling, Richard, Yassi, Nawaf, Oxley, Thomas J, Wu, Teddy Y, Silver, Gabriel, McDonald, Amy, McCoy, Rachael, Kleinig, Timothy J, Scroop, Rebecca, Dewey, Helen M, Simpson, Marion, Brooks, Mark, Coulton, Bronwyn, Krause, Martin, Harrington, Timothy J, Steinfort, Brendan, Faulder, Kenneth, Priglinger, Miriam, Day, Susan, Phan, Thanh, Chong, Winston, Holt, Michael, Chandra, Ronil V, Ma, Henry, Young, Dennis, Wong, Kitty, Wijeratne, Tissa, Tu, Hans, Mackay, Elizabeth, Celestino, Sherisse, Bladin, Christopher F, Loh, Poh Sien, Gilligan, Amanda, Ross, Zofia, Coote, Skye, Frost, Tanya, Parsons, Mark W, Miteff, Ferdinand, Levi, Christopher R, Ang, Timothy, Spratt, Neil, Kaauwai, Lara, Badve, Monica, Rice, Henry, de Villiers, Laetitia, Barber, P. 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Alejandro, Brown, Scott, Liebeskind, David S, Bracard, Serge, Dippel, Diederik W J, Jovin, Tudor G, and Hill, Michael D

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2019
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49. Imaging features and safety and efficacy of endovascular stroke treatment: a meta-analysis of individual patient-level data

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Berkhemer, Olvert A, Fransen, Puck SS, Beumer, Debbie, van den Berg, Lucie A, Lingsma, Hester F, Yoo, Albert J, Schonewille, Wouter J, Vos, Jan Albert, Nederkoorn, Paul J, Wermer, Marieke JH, van Walderveen, Marianne AA, Staals, Julie, Hofmeijer, Jeannette, van Oostayen, Jacques A., Lycklama à Nijeholt, Geert J., Boiten, Jelis, Brouwer, Patrick A., Emmer, Bart J., de Bruijn, Sebastiaan F., van Dijk, Lukas C., Kappelle, Jaap, Lo, Rob H, van Dijk, Ewoud J., de Vries, Joost, de Kort, Paul L.M., van Rooij, Willem Jan J., van den Berg, Jan S.P., van Hasselt, Boudewijn A.A.M., Aerden, Leo A.M., Dallinga, René J., Visser, Marieke C., Bot, Joseph C.J., Vroomen, Patrick C., Eshghi, Omid, Schreuder, Tobien H.C.M.L., Heijboer, Roel J.J., Keizer, Koos, Tielbeek, Alexander V., den Hertog, Heleen M., Gerrits, Dick G., van den Berg-Vos, Renske M., Karas, Giorgos B., Steyerberg, Ewout W., Flach, Zwenneke, Marquering, Henk A., Sprengers, Marieke E.S., Jenniskens, Sjoerd F.M., Beenen, Ludo F.M., van den Berg, René, Koudstaal, Peter J., van Zwam, Wim H., Roos, Yvo B.W.E.M., van der Lugt, Aad, van Oostenbrugge, Robert J., Majoie, Charles B.L.M., Dippel, Diederik W.J., Brown, Martin M., Liebig, Thomas, Stijnen, Theo, Andersson, Tommy, Mattle, Heinrich, Wahlgren, Nils, van der Heijden, Esther, Ghannouti, Naziha, Fleitour, Nadine, Hooijenga, Imke, Puppels, Corina, Pellikaan, Wilma, Geerling, Annet, Lindl-Velema, Annemieke, van Vemde, Gina, de Ridder, Ans, Greebe, Paut, de Bont-Stikkelbroeck, José, de Meris, Joke, Janssen, Kirsten, Struijk, Willy, Licher, Silvan, Boodt, Nikki, Ros, Adriaan, Venema, Esmee, Slokkers, Ilse, Ganpat, Raymie-Jayce, Mulder, Maxim, Saiedie, Nawid, Heshmatollah, Alis, Schipperen, Stefanie, Vinken, Stefan, van Boxtel, Tiemen, Koets, Jeroen, Boers, Merel, Santos, Emilie, Borst, Jordi, Jansen, Ivo, Kappelhof, Manon, Lucas, Marit, Geuskens, Ralph, Barros, Renan Sales, Dobbe, Roeland, Csizmadia, Marloes, Hill, MD, Goyal, M, Demchuk, AM, Menon, BK, Eesa, M, Ryckborst, KJ, Wright, MR, Kamal, NR, Andersen, L, Randhawa, PA, Stewart, T, Patil, S, Minhas, P, Almekhlafi, M, Mishra, S, Clement, F, Sajobi, T, Shuaib, A, Montanera, WJ, Roy, D, Silver, FL, Jovin, TG, Frei, DF, Sapkota, B, Rempel, JL, Thornton, J, Williams, D, Tampieri, D, Poppe, AY, Dowlatshahi, D, Wong, JH, Mitha, AP, Subramaniam, S, Hull, G, Lowerison, MW, Salluzzi, M, Maxwell, M, Lacusta, S, Drupals, E, Armitage, K, Barber, PA, Smith, EE, Morrish, WF, Coutts, SB, Derdeyn, C, Demaerschalk, B, Yavagal, D, Martin, R, Brant, R, Yu, Y, Willinsky, RA, Weill, A, Kenney, C, Aram, H, Stys, PK, Watson, TW, Klein, G, Pearson, D, Couillard, P, Trivedi, A, Singh, D, Klourfeld, E, Imoukhuede, O, Nikneshan, D, Blayney, S, Reddy, R, Choi, P, Horton, M, Musuka, T, Dubuc, V, Field, TS, Desai, J, Adatia, S, Alseraya, A, Nambiar, V, van Dijk, R, Newcommon, NJ, Schwindt, B, Butcher, KS, Jeerakathil, T, Buck, B, Khan, K, Naik, SS, Emery, DJ, Owen, RJ, Kotylak, TB, Ashforth, RA, Yeo, TA, McNally, D, Siddiqui, M, 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