Pediatric Emergency Research Networks
Bibliographic record
Abstract
OBJECTIVES: Objectives of the Pediatric Emergency Research Network's (PERN's) meeting included (1) learn about each of the participating network's missions, goals, and infrastructure; (2) share important contributions each network has made to the creation of new knowledge; (3) discuss "best practices" to improve each network's effectiveness; and (4) explore the potential for a collaborative research project as proof of concept that would help us promote quality of care of the acutely ill and injured child/youth globally. METHODS: In October 2009, a multiday meeting was attended by 18 delegates representing the following pediatric emergency medicine research networks: Pediatric Emergency Medicine Collaborative Research Committee (United States), Pediatric Emergency Care Applied Research Network (United States), Pediatric Emergency Research of Canada (Canada), Paediatric Research in Emergency Departments International Collaborative (Australia and New Zealand), and Research in European Pediatric Emergency Medicine (15 countries in Europe and the Middle East). RESULTS: The inaugural meeting of PERN demonstrated that there is a common desire for high-quality research and the dissemination of this research to improve health and outcomes of acutely ill and injured children and youths throughout the world. Presently, the PERN group is in the final stages of developing a protocol to assess H1N1 risk factors with the collection of retrospective data. CONCLUSIONS: Several members of PERN will be gathering at the International Conference on Emergency Medicine in Singapore, where the group will be presenting information about the H1N1 initiative. The PERN group is planning to bring together all 5 networks later in 2010 to discuss future global collaborations.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.270 | 0.087 |
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".