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Exergaming With Beat Saber: An Investigation of Virtual Reality Aftereffects

Exergaming With Beat Saber: An Investigation of Virtual Reality Aftereffects

The Royal Air Force (RAF) near-point rule [43] was used to assess the near point of convergence and the near point of accommodation before and after VR exposure.

Ancret Szpak, Stefan Carlo Michalski, Tobias Loetscher

J Med Internet Res 2020;22(10):e19840

The Development of the Military Service Identification Tool: Identifying Military Veterans in a Clinical Research Database Using Natural Language Processing and Machine Learning

The Development of the Military Service Identification Tool: Identifying Military Veterans in a Clinical Research Database Using Natural Language Processing and Machine Learning

Most of the words and phrases annotated described the service branch (eg, “served in the army,” “national service in the RAF,” “demobbed from the army,” and “was a pilot in the RAF”), with only a small number including the length of service (eg, “served for

Daniel Leightley, David Pernet, Sumithra Velupillai, Robert J Stewart, Katharine M Mark, Elena Opie, Dominic Murphy, Nicola T Fear, Sharon A M Stevelink

JMIR Med Inform 2020;8(5):e15852

Intellectual Structure and Evolutionary Trends of Precision Medicine Research: Coword Analysis

Intellectual Structure and Evolutionary Trends of Precision Medicine Research: Coword Analysis

ALK: ALK Receptor Tyrosine Kinase; BRAF: v-raf murine sarcoma viral oncogene homolog B1; BRCA: BReast CAncer gene; EGFR: Epidermal growth factor receptor; HER2: Receptor tyrosine-protein kinase erbB-2; NGS: Next-generation sequencing; KRAS: Ki-ras2 Kirsten

Xiaoguang Lyu, Jiming Hu, Weiguo Dong, Xin Xu

JMIR Med Inform 2020;8(2):e11287

Clinical Genomic Sequencing Reports in Electronic Health Record Systems Based on International Standards: Implementation Study

Clinical Genomic Sequencing Reports in Electronic Health Record Systems Based on International Standards: Implementation Study

EthnicityHealth Level 7 (HL7) version 3 code system race2040-4KoreanDiagnosticReportDiagnosticReport.subject(patient).extension(ethnicity)Genetic variationGene symbols and namesHuman Genome Organisation (HUGO) Gene Nomenclature Committee (HGNC)HGNC:1097, B-Raf

Borim Ryu, Soo-Yong Shin, Rong-Min Baek, Jeong-Whun Kim, Eunyoung Heo, Inchul Kang, Joshua SungWoo Yang, Sooyoung Yoo

J Med Internet Res 2020;22(8):e15040

Behavioral, Nutritional, and Genetic Risk Factors of Colorectal Cancers in Morocco: Protocol for a Multicenter Case-Control Study

Behavioral, Nutritional, and Genetic Risk Factors of Colorectal Cancers in Morocco: Protocol for a Multicenter Case-Control Study

Another example demonstrating the importance of considering genetic status is the analysis of certain mutations that occur in the early stages of CRC such as Kirsten rat sarcoma (KRAS) and proto-oncogene B-Raf (BRAF) mutations.

Meimouna Mint Sidi Ould Deoula, Inge Huybrechts, Khaoula El Kinany, Hanae Boudouaya, Zineb Hatime, Achraf El Asri, Abdelilah Benslimane, Chakib Nejjari, Ibrahimi Sidi Adil, Karima El Rhazi

JMIR Res Protoc 2020;9(1):e13998

Digital Monitoring and Management of Patients With Advanced or Metastatic Non-Small Cell Lung Cancer Treated With Cancer Immunotherapy and Its Impact on Quality of Clinical Care: Interview and Survey Study Among Health Care Professionals and Patients

Digital Monitoring and Management of Patients With Advanced or Metastatic Non-Small Cell Lung Cancer Treated With Cancer Immunotherapy and Its Impact on Quality of Clinical Care: Interview and Survey Study Among Health Care Professionals and Patients

Guidelines for patients with locally advanced or metastatic NSCLC without alterations in EGFR (epidermal growth factor receptor), ALK (anaplastic lymphoma kinase), ROS1 (ROS proto-oncogene 1), BRAF (B-Raf proto-oncogene), NTRK (neurotrophic tropomyosin receptor

Oliver Schmalz, Christine Jacob, Johannes Ammann, Blasius Liss, Sanna Iivanainen, Manuel Kammermann, Jussi Koivunen, Alexander Klein, Razvan Andrei Popescu

J Med Internet Res 2020;22(12):e18655

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

45T1Da152810 h✓ANNb[39]201030T1D121024 h✓ANN[33]201075Critical Care11615 h✓ANN[40]201075T1D27524 h✓ANN[41]201130T1D5VPc, 1724 h✓✓ANN[42]201130, 45T1D30VP824 h✓ANN[43]201215, 30, 60, 120T1D271324 h✓✓RFd[44]201230T1D20VP, 911, 724 h✓ANN[45]201230T1D10324 h✓SVMe, RAf

Ivan Contreras, Josep Vehi

J Med Internet Res 2018;20(5):e10775