Correlation of Bedside Pediatric Early Warning System Score to Interventions During Peritransport Period
Bibliographic record
Abstract
BACKGROUND: The Bedside Pediatric Early Warning System score is a validated measure of severity of illness in acute care inpatient settings. Its potential as a remote assessment tool for interfacility transport has not been evaluated. We hypothesized that the Bedside Pediatric Early Warning System score was associated with need for intervention during the peritransport period and patient disposition. METHODS: We retrospectively evaluated children transported by a regional pediatric team during a 6-month period. Bedside Pediatric Early Warning System scores were calculated at the triage phone call, the transport team arrival, and at transfer of care to the hospital team. The primary outcome was the receipt of significant intervention during the peritransport period, with additional outcomes of destination (ICU, ward, emergency department) in the regional hospital. Scores are presented as median values (interquartile range). RESULTS: There were 564 children who underwent transport; 139 (25%) received interventions; and 205 (36%) were transferred to the PICU, 231 (41%) to the ward, and 127 (23%) to the emergency department. Scores were 2 (1-5; median interquartile range) in children receiving no in-transport interventions, 8 (5-11) in children receiving any intervention (p < 0.001), and 10 (7-14) in children receiving more than one intervention. Children transferred to the PICU had higher scores 6 (3-10), than children transferred to a ward 3 (1-6) or the emergency department 2 (1-3) (p < 0.001). CONCLUSIONS: The Bedside Pediatric Early Warning System score at the time of initial referral is a useful measure of severity of illness reflected by the subsequent provision of significant peritransport intervention and the transfer destination.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".