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1. Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer

4. Performance/outcomes data and physician process challenges for practical big data efforts in radiation oncology

9. 62 Operational Ontology for Radiation Oncology (OORO) - a Professional Society-Based, Multi-Stakeholder Consensus Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data

11. 2851: Operational Ontology for Oncology (O3) - Multi-professional society standard supporting AI

12. Directly Improving the Quality of Radiation Treatment Through Peer Review: A Cross-sectional Analysis of Cancer Centers Across a Provincial Cancer Program

14. Head and Neck Radiation Therapy Patterns of Practice Variability Identified as a Challenge to Real-World Big Data: Results From the Learning from Analysis of Multicentre Big Data Aggregation (LAMBDA) Consortium

16. 112: An”Early Adopter” of CPQR’s Patient Reported Outcome (PRO) Initiative: Lessons Learned Through Implementation of an Electronic PRO Platform Across a Mulit-Center Radiation Oncology Department

17. Virtually, Anything is Possible: Implementation of an Electronic Patient Reported Outcome Platform Across A Multi-Center Radiation Oncology Department

18. Striving to Fill in Gaps between Clinical Practice and Standards: The Evolution of a Pan-Canadian Approach to Patient-Reported Outcomes Use

26. In Reply to Wang and Rallis

28. Contributors

33. 183: Head and Neck Radiotherapy (RT) Patterns of Practice Variability Identified as a Challenge to Real-World Big Data: Recommendations from the Learning from Analysis of Multicentre Big Data Aggregation (Lambda) Consortium

37. 144: A Pan-Canadian Approach to Supporting The Collection and Utilization of Patient Reported Outcomes in Radiation Oncology: An Initiative of the Canadian Partnership for Quality Radiotherapy

39. Analysis of exhaled breath of lung cancer patients using infrared spectroscopy.

43. 38 Early Dosimetric Findings from the Learning from Analysis of Multicentre Big Data Aggregation (LAMBDA) Consortium

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