Pediatric Emergency Research Canada (<scp>PERC</scp>): Patient/Family‐informed Research Priorities for Pediatric Emergency Medicine
Bibliographic record
Abstract
BACKGROUND: A growing body of literature supports patient and public involvement in the design, prioritization, and dissemination of research and evidence-based medicine. The objectives of this project were to engage patients and families in developing a prioritized list of research topics for pediatric emergency medicine (PEM) and to compare results with prior research prioritization initiatives in the emergency department (ED) setting. METHODS: We utilized a systematic process to combine administrative data on frequency of patient presentations to the ED with multiple stakeholder input including an initial stakeholder survey followed by a modified Delphi consensus methodology consisting of two Web-based surveys and a face-to-face meeting. RESULTS: The prioritization process resulted in a ranked list of 15 research priorities. The top five priorities were mental health presentations, pain and sedation, practice tools, quality of care delivery, and resource utilization. Mental health, pain and sedation, clinical prediction rules, respiratory illnesses/wheeze, patient safety/medication error, and sepsis were identified as shared priorities with prior initiatives. Topics identified in our process that were not identified in prior work included resource utilization, ED communication, antibiotic stewardship, and patient/family adherence with recommendations. CONCLUSIONS: This work identifies key priorities for research in PEM. Comparing our results with prior initiatives in the ED setting identified shared research priorities and opportunities for collaboration among PEM research networks. This work in particular makes an important contribution to the existing literature by including the patient/family perspective missing from prior work.
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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".