Comparison of Specialist and Nonspecialist Transport Teams for Emergency Neurosurgery
Bibliographic record
Abstract
OBJECTIVES: Current guidance in the United Kingdom recommends that children requiring emergency neurosurgical intervention should be transported by referring hospital (RH) teams. We aimed to compare transports performed by RH teams and by specialized pediatric critical care transport (PCCTs) teams in terms of timings and patient outcomes. METHODS: We conducted a retrospective analysis over a 5-year period of children admitted from an external hospital to the pediatric intensive care unit at a pediatric neurosurgical center and receiving emergency neurosurgery within 24 hours of admission. Data were collected on RH characteristics, patient demographics, clinical status, transfer method (RH or PCCT team), timings (arrival at neurosurgical center, neurosurgical procedure), and clinical outcomes (length of stay and mortality). Univariate analysis was used to compare patient characteristics, times, and outcomes between RH and PCCT team transfers. Survival analysis was performed to analyze arrival time by transfer modality. RESULTS: During the study period, 75 children with acute neurosurgical emergencies were transferred. Median age was 6.7 years (interquartile range, 1.8-10.7), and 63% had nontraumatic diagnoses. The commonest mode of transfer was by RH teams after initial referral to a PCCT team (53.3%). The median distance was greatest for transfers by RH teams (14 km). Overall median arrival time was 5 hours (interquartile range, 3.6-7.4) with no significant difference between groups ( P = 0.3). Median length of pediatric intensive care unit stay and mortality did not differ between groups. CONCLUSIONS: Specialist critical care transport teams are involved in one third of transfers of children with acute neurosurgical emergencies. While the overriding priority is timely transfer, a tailored approach to the use of PCCTs may be appropriate particularly for children presenting to hospitals nearer to neurosurgical centers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".