Highlights of the 2020 23rd Society for Cardiovascular Magnetic Resonance Scientific Sessions
Bibliographic record
Abstract
Cardiovascular magnetic resonance (CMR) is an integral part of the evaluation and management of patients with cardiovascular disease. As a result, the interest in this modality among scientific and clinical communities continues to grow, as evidenced by a record number of attendees at the 23rd Society for Cardiovascular Magnetic Resonance (SCMR) Annual Scientific Sessions. More than 1,440 delegates from around the world experienced the meeting in Orlando, Florida, USA from February 12–15, 2020 (Fig. 1 ). Two thirds of attendees were from North America and a fifth from Europe. Cardiologists constituted the most prevalent professional designation which is a testament to the important role of CMR in routine clinical cardiology practice and cardiology research. The theme of the meeting was entitled, “Transforming Cardiovascular Care Through Discoveries in Imaging” (Fig. 2 ). A total of 112 sessions spanned the spectrum from technical developments to basic discoveries, clinical science and advocacy. The meeting was preceded by a joint workshop together with the International Society for Magnetic Resonance in Medicine (ISMRM) and several preconference courses, including a general overview of CMR techniques and applications for physicians, a course around pediatric and congenital heart disease, and a review of the fundamentals of cardiology. The theme of the SCMR/ISMRM workshop was “CMR 4.0: Autonomous and Efficient Cardiovascular Magnetic Resonance Imaging”. This 1.5 day event focused on the topics of ‘efficient and effective CMR’, ‘autonomous scanning and quantification’, ‘hardware innovations’, as well as ‘artificial intelligence (AI)’ and ‘big data’. A complete course on interventional CMR was also offered. Fifty-seven abstract and didactic sessions during the main meeting highlighted recent CMR technical and clinical developments as well as scientific discoveries. These ranged from improving quantitative precision for diagnosis of cardiovascular disease in an individual patient to identifying methods of using CMR to improve outcomes. Within this context, sessions showcased work from around the world using CMR in congenital heart disease, ischemic and non-ischemic heart disease, valvular disease, electrophysiology, systemic inflammation, cardio-oncology, heart failure, and vascular pathology. This report highlights a few of the contributions in the areas of ‘Innovative Clinical Applications of CMR’, ‘Clinical and Translational Science’, and ‘Basic Science & Technical Developments’. Table 1 lists the scientistis, clinicians, and abstracts that were specifically recognized during the meeting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.114 | 0.089 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".