Pediatric Transport Triage
Bibliographic record
Abstract
OBJECTIVES: We developed a Pediatric Transport Triage Tool (PT3) to objectively guide selection of team composition and transport mode, thereby standardizing transport planning. Previously, modified Pediatric Early Warning Score for transport has been used to assess illness severity but not to guide transport decision making. METHODS: The PT3 was created for pediatric transport by combining objective evaluations of neurologic, cardiovascular, and respiratory systems with a systems-based medical condition list to identify diagnoses requiring expedited transport and/or advanced team composition not captured by neurologic, cardiovascular, and respiratory systems alone. A scoring algorithm was developed to guide transport planning. Transport data (mode, team composition, time to dispatch, patient disposition, and complications) were collected before and after PT3 implementation at a single tertiary care center over an 18-month period. RESULTS: We reviewed 2237 inbound pediatric transports. Transport mode, patient disposition, and dispatch time were unchanged over the study period. Fewer calls using a transport nurse were noted after PT3 implementation (33.9% vs 30%, P = 0.05), with a trend toward fewer rotor-wing transports and transports requiring physicians. The majority of users, regardless of experience level, reported improved transport standardization with the tool. Need to upgrade team composition or mode during transport was not different during the study period. No adverse patient safety events occurred with PT3 use. CONCLUSIONS: The PT3 represents an objective triage tool to reduce variability in transport planning. The PT3 decreased resource utilization and was not associated with adverse outcomes. Teams with dynamic staffing models, various experience levels, and multiple transport modes may benefit from this standardized assessment tool.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".