The Pediatric Emergency Research Network
Bibliographic record
Abstract
OBJECTIVES: The Pediatric Emergency Research Network (PERN) was launched in 2009 with the intent for existing national and regional research networks in pediatric emergency care to organize globally for the conduct of collaborative research across networks. METHODS: The Pediatric Emergency Research Network has grown from 5- to 8-member networks over the past decade. With an executive committee comprising representatives from all member networks, PERN plays a supportive and collaborative rather than governing role. The full impact of PERN's facilitation of international collaborative research, although somewhat difficult to quantify empirically, can be measured indirectly by the observed growth of the field, the nature of the increasingly challenging research questions now being addressed, and the collective capacity to generate and implement new knowledge in treating acutely ill and injured children. RESULTS: Beginning as a pandemic response with a high-quality retrospective case-controlled study of H1N1 influenza risk factors, PERN research has progressed to multiple observational studies and ongoing global randomized controlled trials. As a recent example, PERN has developed sufficient network infrastructure to enable the rapid initiation of a prospective observational study in response to the current coronavirus disease 2019 pandemic. In light of the ongoing need for translation of research knowledge into equitable clinical practice and to promote health equity, PERN is committed to a coordinated international effort to increase the uptake of evidence-based management of common and treatable acute conditions in all emergency department settings. CONCLUSIONS: The Pediatric Emergency Research Network's successes with global research, measured by prospective observational and interventional studies, mean that the network can now move to improve its ability to promote the implementation of scientific advances into everyday clinical practice. Achieving this goal will involve focus in 4 areas: (1) expanding the capacity for global randomized controlled trials; (2) deepening the focus on implementation science; (3) increasing attention to healthcare disparities and their origins, with growing momentum toward equity; and (4) expanding PERN's global reach through addition of sites and networks from resource-restricted regions. Through these actions, PERN will be able to build on successes to face the challenges ahead and meet the needs of acutely ill and injured children throughout the world.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.196 | 0.093 |
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".