PRELIMINARY RESULTS OF A PHASE 1 DOSE ESCALATION STUDY OF THE FIRST‐IN‐CLASS ANTI‐CD74 ANTIBODY DRUG CONJUGATE (ADC), STRO‐001, IN ADVANCED B‐CELL MALIGNANCIES von Molina, A., Shah, N., Krishnan, A.Y., Shah, N.D., Burke, J.M., Melear, J.M., Spira, A.I., Popplewell, L.L., Andreadis, C.B., Chhabra, S., Sharman, J.P., Kaufman, J.L., Cohen, J.B., Niesvizky, R., Martin, T.G., DiLea, C., Kuriakose, J., Matheny, S.L., Leonard, J.P.

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Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction von Song, Xing, Yu, Alan S. L., Kellum, John A., Waitman, Lemuel R., Matheny, Michael E., Simpson, Steven Q., Hu, Yong, Liu, Mei

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Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership von FitzHenry, F., Resnic, F.S., Robbins, S.L., Denton, J., Nookala, L., Meeker, D., Ohno-Machado, L., Matheny, M.E.

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Development and Validation of a Personalized Model With Transfer Learning for Acute Kidney Injury Risk Estimation Using Electronic Health Records von Liu, Kang, Zhang, Xiangzhou, Chen, Weiqi, Yu, Alan S. L., Kellum, John A., Matheny, Michael E., Simpson, Steven Q., Hu, Yong, Liu, Mei

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Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network von Williams, R.D., Markus, A.F., Yang, C., Duarte-Salles, T., DuVall, S.L., Falconer, T., Jonnagaddala, J., Kim, C., Rho, Y., Williams, A.E., Machado, A.A., An, M.H., Aragon, M., Areia, C., Burn, E., Choi, Y.H., Drakos, I., Abrahao, M.T.F., Fernandez-Bertolin, S., Hripcsak, G., Kaas-Hansen, B.S., Kandukuri, P.L., Kors, J.A., Kostka, K., Liaw, S.T., Lynch, K.E., Machnicki, G., Matheny, M.E., Morales, D., Nyberg, F., Park, R.W., Prats-Uribe, A., Pratt, N., Rao, G., Reich, C.G., Rivera, M., Seinen, T., Shoaibi, A., Spotnitz, M.E., Steyerberg, E.W., Suchard, M.A., You, S.C., Zhang, L., Zhou, L.L., Ryan, P.B., Prieto-Alhambra, D., Reps, J.M., Rijnbeek, P.R.

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Implementation of the COVID-19 vulnerability index across an international network of health care data sets: collaborative external validation study von Reps, J.M., Kim, C., Williams, R.D., Markus, A.F., Yang, C., Duarte-Salles, T., Falconer, T., Jonnagaddala, J., Williams, A., Fernandez-Bertolin, S., DuVall, S.L., Kostka, K., Rao, G., Shoaibi, A., Ostropolets, A., Spotnitz, M.E., Zhang, L., Casajust, P., Steyerberg, E.W., Nyberg, F., Kaas-Hansen, B.S., Choi, Y.H., Morales, D., Liaw, S.T., Abrahao, M.T.F., Areia, C., Matheny, M.E., Lynch, K.E., Aragon, M., Park, R.W., Hripcsak, G., Reich, C.G., Suchard, M.A., You, S.C., Ryan, P.B., Prieto-Alhambra, D., Rijnbeek, P.R.

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Classification of chromatic visual evoked potentials with the aid of a neural net von Swihart, S.L., Matheny, A.

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