The Pediatric Emergency Research Network (<scp>PERN</scp>): A decade of global research cooperation in paediatric emergency care
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 paediatric emergency care to organise globally for the conduct of collaborative research across networks. METHODS: PERN has grown from five to eight 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 studying H1N1 influenza risk factors in children, PERN research has progressed to multiple observational studies and ongoing global randomised controlled trials (RCTs). 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 COVID-19 pandemic. CONCLUSIONS: Following its success with developing global research, the PERN goal now is to promote the implementation of scientific advances into everyday clinical practice by: (i) expanding the capacity for global RCTs; (ii) deepening the focus on implementation science; (iii) increasing attention to healthcare disparities; and (iv) expanding PERN's reach into resource-restricted regions. Through these actions, PERN aims to meet the needs of acutely ill and injured children throughout the world.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".