Hospital outcomes of children admitted to intensive care in British Columbia via interfacility transfer versus direct admission from 2015 to 2017: a descriptive analysis
Bibliographic record
Abstract
Background: Pediatric intensive care relies on having experienced and effective transport systems to transfer critically ill children to the appropriate centre for care. Our aim was to compare hospital outcomes among children admitted directly to a pediatric intensive care unit (PICU) with those of children transferred from another facility. Methods: We conducted a descriptive study using electronic medical records and the PICU database from the BC Children’s Hospital. Patients admitted to the PICU from January 2015 to December 2017 were included. We excluded patients who were admitted electively, were admitted for recovery postoperatively, or had inconsistent or out-of-range addresses. We compared hospital mortality rates, use of mechanical ventilation within 24 hours of admission and length of PICU stay between children admitted directly from the BC Children’s Hospital emergency department and those transferred from a referring institution. Results: During the study period, there were 870 unique admissions comprising 386 direct admissions and 484 transferred patients. Transported patients were younger, were more critically ill on presentation and required longer stays. The proportions of children who died and of children who required mechanical ventilation within 24 hours of admission were higher in the transported group than in the group admitted directly from the emergency department (8.3% v. 3.9%, p = 0.008, and 75.8% v. 58.0%, p < 0.001, respectively). Interpretation: Mortality rate and use of intensive care resources were higher among children who were transported. Further research is needed to examine the key factors driving the differences in outcomes, including the severity of illness on first presentation, transport team composition, and transport distance and duration.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".