What Are the Characteristics andOutcomes of Nontransported Pediatric Patients?
Bibliographic record
Abstract
BACKGROUND: The authors have demonstrated that 28% of children cared for by paramedics are not transported to hospital by ambulance. OBJECTIVE: To determine the characteristics, reasons, and outcomes for this nontransported population. METHODS: This was a prospective cohort study in a single city with a two-tiered emergency medical services system. Enrolled were all children aged < 16 years assessed by paramedics but not transported to hospital over a five-month period. Data were collected from ambulance call reports, phone interviews, and hospital charts. Descriptive statistics were used. RESULTS: Over five months, there were 345 nontransported pediatric patients with a mean age of 6 years, and 58.3% were male. The dispatch priority was urgent in 68.1% of cases and prompt in 30.4% of cases. The primary problems were almost evenly split between trauma (50.7%) and medical (45.2%) causes. Paramedics listed the following reasons for nontransport: parent will take the child to a physician (27.8%), parent will monitor the child's condition (25.8%), and no reason documented (46.4%). Phone interview was conducted with 106 parents (30.7%): 76.4% believed there was a true emergency at the time of the 9-1-1 call, 75.5% stated that the paramedics did not recommend that the child be transported to hospital by ambulance, and 29.2% stated that the paramedics said ambulance transport was not necessary. Fifty-one children were seen in an emergency department (ED) within 48 hours of the 9-1-1 call. The majority (91.3%) were discharged home from the ED, while a small minority (8.7%) were admitted to hospital. No deaths were reported. CONCLUSIONS: Most nontransported children did not require immediate or urgent medical care. Both parents and paramedics gave input into the nontransport decision, and the short-term outcome of this population appeared to be good. Paramedic documentation for the reasons for nontransport should be improved.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".