Saving Lives and Improving the Quality of Pediatric Resuscitation Across the World
Bibliographic record
Abstract
STATEMENT: The International Network for Simulation-based Pediatric Innovation, Research, and Education co-hosted a novel research accelerator meeting with the International Pediatric Simulation Society in May of 2019 in Toronto. The purpose of the meeting was to bring together healthcare simulation scientists with resuscitation stakeholders to brainstorm strategies for accelerating progress in the science of saving pediatric lives from cardiac arrest. This was achieved by working in teams to draft targeted requests for proposals calling the research community to action investigating this topic. During the 1-day meeting, groups were divided into 6 teams lead by experts representing specific domains of simulation research. Teams developed a pitch and presented a sample request for proposals to a panel of expert judges, making a case for why their domain was the most important to create a funding opportunity. The winner of the competition had their specific request for proposal turned into an actual funding opportunity, supported by philanthropy that was subsequently disseminated through International Network for Simulation-based Pediatric Innovation, Research, and Education as a competitive award. An inspired donor supported an award for the second-place proposal as well, evidence of early research acceleration catalyzed from this conference. This article is a summary of the meeting rationale, format, and a description of the requests for proposals that emerged from the meeting. Our goal is to inspire other stakeholders to use this document that leverages simulation and resuscitation science expertise, as the framework to create their own funding opportunities, further accelerating pediatric resuscitation research, ultimately saving the lives of more children worldwide.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".