Identifying Significant and Relevant Events During Pediatric Transport
Bibliographic record
Abstract
OBJECTIVES: Children must often be transported to dedicated pediatric centers to receive specialized medical and surgical care, which places them at risk for significant deterioration and life-threatening events. Studies designed to identify and mitigate these events have been limited by variability in the selection and definition of significant events. The objective of this study was to identify and evaluate indicators that represent significant events during the transport of pediatric patients and are relevant to future research initiatives in transport medicine. DESIGN: We conducted a modified Delphi study consisting of four iterations. SETTING: The expert panel included Canadian, interdisciplinary healthcare providers with transport experience. INTERVENTIONS: In the first Delphi iteration, experts suggested indicators for consideration and evaluated proposed indicators from the literature and introduced by the study steering committee. In subsequent iterations, respondents reevaluated all indicators that had not yet achieved a priori-defined consensus; group comments and aggregate scores for each indicator from previous iterations were provided. MEASUREMENTS AND MAIN RESULTS: The expert panel consisted of 16 physicians and 17 nonphysician healthcare providers from 10 Canadian institutions. In total, the panel evaluated 57 indicators, including 26 not previously presented in the literature. The expert panel determined 52 were significant and relevant to future studies in pediatric transport. The final indicator list includes trigger tools (interventions, physiological markers, and laboratory values) and team member safety and process issues. CONCLUSIONS: Using a systematic, modified Delphi approach, we developed an inclusive list of indicators for application to pediatric transport-related quality improvement and clinical research projects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".