The Latest in Resuscitation Research: Highlights From the 2020 American Heart Association's Resuscitation Science Symposium
Bibliographic record
Abstract
cardiac arrest ■ cardiopulmonary resuscitation ■ science communication ■ trauma T he first virtual Resuscitation Science Symposium (ReSS) was held November 14 to 16, 2020, organized by volunteers and staff of the American Heart Association (AHA).Through live and pre-recorded sessions, ReSS provided a forum for scientific collaboration, exchange of ideas, and for discussion of the latest developments in resuscitation research. AWARDSThe Young Investigator Awards were presented to early career researchers within the first 5 years of their appointments, recognizing outstanding contributions to research in resuscitation science research (Table S1).The Lifetime Achievement Award in Cardiac Resuscitation Science was presented to Peter Morley, MBBS from the University of Melbourne for his extensive work shaping the evidence review process for the international resuscitation guidelines and leading critical care education initiatives over decades of work.The Ian G. Jacobs Award for Group Collaboration to Advance Resuscitation Science was presented to Take Heart America, an effort across multiple communities to promote high-quality resuscitation care and support research into advanced strategies for cardiopulmonary resuscitation (CPR) delivery.The ReSS Champion Award for contributions to the field through research and clinical improvements in government, industry or public advocacy was awarded to Ann Doll, BA from Resuscitation Academy based in Seattle, Washington.Ms. Doll helped establish and lead programs to improve Emergency Medical Services care delivery during cardiac arrest, helping grow the Resuscitation Academy into a global force for cardiac arrest care training.The Max Harry Weil Award for Resuscitation Science, to recognize outstanding published work from a junior investigator, was presented to Tasuku Matsuyama, MD PhD, from the Kyoto Prefectural University of Medicine, for his work evaluating the impact of epinephrine on pediatric cardiac arrest outcomes.
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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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".