Interhospital Transport of Critically Ill Children to PICUs in the United Kingdom and Republic of Ireland: Analysis of an International Dataset*
Bibliographic record
Abstract
OBJECTIVES: International data on characteristics and outcomes of children transported from general hospitals to PICUs are scarce. We aimed to 1) describe the development of a common transport dataset in the United Kingdom and Ireland and 2) analyze transport data from a recent 2-year period. DESIGN: Retrospective analysis of prospectively collected data. SETTING: Specialist pediatric critical care transport teams and PICUs in the United Kingdom and Ireland. PATIENTS: Critically ill children less than 16 years old transported by pediatric critical care transport teams to PICUs in the United Kingdom and Ireland. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A common transport dataset was developed as part of the Paediatric Intensive Care Audit Network, and standardized data were collected from all PICUs and pediatric critical care transport teams from 2012. Anonymized data on transports (and linked PICU admissions) from a 2-year period (2014-2015) were analyzed to describe patient and transport characteristics, and in uni- and multivariate analyses, to study the association between key transport factors and PICU mortality. A total of 8,167 records were analyzed. Transported children were severely ill (median predicted mortality risk 4.4%) with around half being infants (4,226/8,167; 51.7%) and nearly half presenting with respiratory illnesses (3,619/8,167; 44.3%). The majority of transports were led by physicians (78.4%; consultants: 3,059/8,167, fellows: 3,344/8,167). The median time for a pediatric critical care transport team to arrive at the patient's bedside from referral was 85 minutes (interquartile range, 58-135 min). Adverse events occurred in 369 transports (4.5%). There were considerable variations in how transports were organized and delivered across pediatric critical care transport teams. In multivariate analyses, consultant team leader and transport from an intensive care area were associated with PICU mortality (p = 0.006). CONCLUSIONS: Variations exist in United Kingdom and Ireland services for critically ill children needing interhospital transport. Future studies should assess the impact of these variations on long-term patient outcomes taking into account treatment provided prior to transport.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".