Frequency, Composition, and Predictors of In-Transit Critical Events During Pediatric Critical Care Transport*
Bibliographic record
Abstract
OBJECTIVES: Transport of pediatric patients is common due to healthcare regionalization. We set out to determine the frequency of in-transit critical events during pediatric critical care transport and identify factors associated with these events. DESIGN: Retrospective cohort study using administrative and clinical data. SETTING: Single pediatric critical care transport provider in Ontario, Canada. PATIENTS: All pediatric care transports between January 1, 2005, and December 31, 2010. MEASUREMENTS AND MAIN RESULTS: The primary outcome was in-transit critical events, defined by an adaptation of a recent consensus definition. In-transit critical events occurred in 1,094 (12.3%) of 8,889 transports. Hypotension (3.6%), tachycardia (3.7%), and bradycardia (3.3%) were the most common critical events. Crews performed medical interventions in 194 transports (2.2%). The frequency and makeup of critical events varied across patient age groups. Age, pretransport mechanical ventilation, pretransport cardiovascular instability, transport duration, scene calls, and paramedic crew level were independently associated with increased risk of in-transit critical events in multivariate analysis. A Transport Pediatric Early Warning Score of 7 or greater predicted in-transit critical events with high specificity but low sensitivity (92.0% and 20.0%, respectively), but was not superior of the combination of pretransport mechanical ventilation and pretransport cardiovascular instability (sensitivity and specificity of 12.6% and 97.4%, respectively). Removal of early warning signs from the definition resulted in critical event rates comparable to those published in adults and improved predictive performance. CONCLUSIONS: Using new consensus definitions of transport-related critical events, we found critical events occurred in almost one in eight transports, and were strongly associated with pretransport cardiovascular instability. Transport Pediatric Early Warning Score was poorly predictive of in-transit critical events, and was not superior to the presence of pretransport mechanical ventilation and cardiovascular instability. Future prospective studies are required to elucidate the optimal matching of transport resources to patients, in particular those with both pretransport cardiovascular instability and mechanical ventilation.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".