Severity of Illness Scoring for Pediatric Interfacility Transport
Bibliographic record
Abstract
OBJECTIVE: Severity of illness scoring during pediatric critical care transport may provide objective data to determine illness trajectory and disposition and contribute to quality assurance data for pediatric transport programs. The objective of this study was to ascertain the breadth of severity of illness scoring tool application among North American pediatric critical care transport teams. METHODS: A cross-sectional quantitative survey using REDCap was distributed to 137 North American pediatric transport programs. Baseline team characteristics were established along with questions related to severity of illness tool application.Descriptive statistics were used for analysis. RESULTS: There were 55 responses (40%), and of those, 13 (24%) use a severity of illness scoring tool within their practice. A variety of tools were used including: Transport Risk Index of Physiologic Stability, Children's Hospital Medical Center Cincinnati, Canadian Triage and Acuity Score, Transport Risk Assessment in Pediatrics, Pediatric Early Warning Scores, Levels of Acuity, Transport Pediatric Early Warning Scores, and an unspecified tool. The timing of scoring, team personnel who applied the score, and the frequency of analysis varied between transport programs. CONCLUSIONS: Severity of illness scoring is not consistently performed by pediatric interfacility transport programs in North America. Among the programs that use a scoring tool, there is variability in its application. There is no universally accepted or performed severity of illness scoring tool for pediatric interfacility transport.Future research to validate and standardize a pediatric transport severity of illness scoring tool for North America is necessary.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".