International Medical Graduates in Urology: Tracking Success Through Research.

David Eugenio Hinojosa-Gonzalez,Shane Kronstedt,Gal Saffati,Wesley A. Mayer,Ruslan Korets,Dimitar Zlatev,Anton Wintner,Brian H. Eisner

UROLOGY PRACTICE（2024）

Baylor Coll Med | Beth Israel Deaconess Med Ctr | Harvard Med Sch

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No AccessUrology PracticeUrology Practice Perspectives1 Mar 2024International Medical Graduates in Urology: Tracking Success Through Research David Eugenio Hinojosa-Gonzalez, Shane Kronstedt, Gal Saffati, Wesley A. Mayer, Ruslan Korets, Dimitar Zlatev, Anton Wintner, and Brian H. Eisner David Eugenio Hinojosa-GonzalezDavid Eugenio Hinojosa-Gonzalez *Corresponding Author: David Eugenio Hinojosa-Gonzalez, MD, Scott Department of Urology, Baylor College of Medicine, 7200 Cambridge St, Houston, TX 77030 ( E-mail Address: [email protected] https://orcid.org/0000-0002-0233-0607 Scott Department of Urology, Baylor College of Medicine, Houston, Texas Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts , Shane KronstedtShane Kronstedt https://orcid.org/0000-0003-0757-451X Scott Department of Urology, Baylor College of Medicine, Houston, Texas , Gal SaffatiGal Saffati https://orcid.org/0009-0005-3053-5828 Scott Department of Urology, Baylor College of Medicine, Houston, Texas , Wesley A. MayerWesley A. Mayer Scott Department of Urology, Baylor College of Medicine, Houston, Texas , Ruslan KoretsRuslan Korets Division of Urologic Surgery, Beth Israel Deaconess Medical Center, Boston, Massachusetts , Dimitar ZlatevDimitar Zlatev Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts , Anton WintnerAnton Wintner Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts , and Brian H. EisnerBrian H. Eisner Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts View All Author Informationhttps://doi.org/10.1097/UPJ.0000000000000501AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail References 1. National Resident Matching Program. Charting the Outcomes of the Match—International Medical Graduates. National Resident Matching Program; 2021. Accessed March 3, 2022. https://www.nrmp.org/wp-content/uploads/2021/08/Charting-Outcomes-in-the-Match-2020_IMG_final.pdf Google Scholar 2. American Urological Association. 2021 Urology Residency Match Statistics.American Urological Association. Accessed March 3, 2022. https://www.auanet.org/documents/education/specialty-match/2021-Urology-Residency-Match-Statistics.pdf Google Scholar 3. . A comparison of objective assessment data for the United States and international medical graduates in a general surgery residency. J Surg Educ. 2017; 74(6):e1-e7. Crossref, Medline, Google Scholar 4. . Comparison of surgical residency applicants from U.S. medical schools with U.S.-born and foreign-born international medical school graduates. J Surg Educ. 2008; 65(6):406-412. Crossref, Medline, Google Scholar 5. . A survey of urology residency program directors assessing criteria to interview applicants during the COVID-19 pandemic. Urol Pract. 2021; 8(4):472-479. Link, Google Scholar 6. . Value of research years for international medical graduates applying to general surgery residency. J Surg Educ. 2020; 77(6):1350-1356. Crossref, Medline, Google Scholar 7. . Applicant characteristics associated with selection for ranking at independent surgery residency programs. J Surg Educ. 2015; 72(6):e123-e129. Crossref, Medline, Google Scholar 8. . The path to U.S. neurosurgical residency for foreign medical graduates: trends from a decade 2007-2017. World Neurosurg. 2020; 137:e584-e596. Crossref, Medline, Google Scholar 9. . The unkindest cut of all: are international medical school graduates subjected to discrimination by general surgery residency programs?. Curr Surg. 2002; 59(2):228-236. Crossref, Medline, Google Scholar 10. . International medical graduates are comparable to American medical graduates as general surgery interns. J Surg Res. 2021; 258:239-245. Crossref, Medline, Google Scholar 11. . Comparing international and United States undergraduate medical education and surgical outcomes using a refined balance matching methodology. Ann Surg. 2017; 265(5):916-922. Crossref, Medline, Google Scholar 12. . Evaluating the quality of care provided by graduates of international medical schools. Health Aff. 2010; 29(8):1461-1468. Crossref, Google Scholar Support: None. Conflict of Interest Disclosures: The Authors have no conflicts of interest to disclose. Ethics Statement: This study was deemed exempt from Institutional Review Board review. Author Contributions: Conception and design: D.H.G., D.Z., B.H.E.; Data analysis and interpretation: D.H.G., G.S., R.K., S.K., W.M., D.Z., B.H.E.; Critical revision of the manuscript for scientific and factual content: D.H.G., G.S., R.K., S.K., W.M., D.Z., B.H.E.; Drafting the manuscript: D.H.G., G.S., S.K., D.Z., B.H.E.; Statistical analysis: D.H.G., G.S., S.K., D.Z., B.H.E.; Supervision: D.H.G., G.S., R.K., S.K., W.M., D.Z., B.H.E. Data Availability: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. © 2024 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 11Issue 2March 2024Page: 254-256 Advertisement Copyright & Permissions© 2024 by American Urological Association Education and Research, Inc.Metrics Author Information David Eugenio Hinojosa-Gonzalez Scott Department of Urology, Baylor College of Medicine, Houston, Texas Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts *Corresponding Author: David Eugenio Hinojosa-Gonzalez, MD, Scott Department of Urology, Baylor College of Medicine, 7200 Cambridge St, Houston, TX 77030 ( E-mail Address: [email protected] More articles by this author Shane Kronstedt Scott Department of Urology, Baylor College of Medicine, Houston, Texas More articles by this author Gal Saffati Scott Department of Urology, Baylor College of Medicine, Houston, Texas More articles by this author Wesley A. Mayer Scott Department of Urology, Baylor College of Medicine, Houston, Texas More articles by this author Ruslan Korets Division of Urologic Surgery, Beth Israel Deaconess Medical Center, Boston, Massachusetts More articles by this author Dimitar Zlatev Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts More articles by this author Anton Wintner Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts More articles by this author Brian H. Eisner Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts More articles by this author Expand All Support: None. Conflict of Interest Disclosures: The Authors have no conflicts of interest to disclose. Ethics Statement: This study was deemed exempt from Institutional Review Board review. Author Contributions: Conception and design: D.H.G., D.Z., B.H.E.; Data analysis and interpretation: D.H.G., G.S., R.K., S.K., W.M., D.Z., B.H.E.; Critical revision of the manuscript for scientific and factual content: D.H.G., G.S., R.K., S.K., W.M., D.Z., B.H.E.; Drafting the manuscript: D.H.G., G.S., S.K., D.Z., B.H.E.; Statistical analysis: D.H.G., G.S., S.K., D.Z., B.H.E.; Supervision: D.H.G., G.S., R.K., S.K., W.M., D.Z., B.H.E. Data Availability: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Advertisement PDF downloadLoading ...

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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