The Latest in Resuscitation Science Research: Highlights From the 2018 American Heart Association's Resuscitation Science Symposium
Bibliographic record
Abstract
T his year's American Heart Association (AHA)'s Resusci- tation Science Symposium (ReSS), held November 9 to 11, 2018, in Chicago, Illinois, brought together thoughtprovoking research from basic science to clinical trials and frontline work in the public health space.Across 16 sessions, >50 oral presentations were given on topics ranging from a first-person narrative from a patient's perspective of surviving cardiac arrest to the transcriptional profiling of the neuroprotective mechanisms of inhaled nitric oxide in pediatric arrest.A total of 275 posters and 27 oral presentations on 40 topics were presented.1 Nine awards, including the inaugural winners of the Resuscitation Champion Award, were given. 2 AwardsThe Young Investigator Awards were presented to 10 investigators within the first 5 years of their appointments, honoring exemplary contributions to research in a broad range of topics (Table S1).Lifetime Achievement Awards were presented to 2 clinician-researchers for their life-long work and contributions to the field: Clifton W.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".