Implementing the Bedside Paediatric Early Warning System in a community hospital: A prospective observational study
Bibliographic record
Abstract
BACKGROUND: Late transfer of children with critical illness from community hospitals undermines the advantages of community-based care. It was hypothesized that implementation of the Bedside Paediatric Early Warning System (Bedside PEWS) would reduce late transfers. METHODS: A prospective before-and-after study was performed in a community hospital 22-bed inpatient paediatric ward. The primary outcome, significant clinical deterioration, was a composite measure of circulatory and respiratory support before transfer. Secondary outcomes were stat calls and resuscitation team calls, paediatrician workload and perceptions of frontline staff. RESULTS: Care was evaluated for 842 patient-days before and 2350 patient-days after implementation. The median inpatient census was 13. Implementation of the Bedside PEWS was associated with fewer stat calls to paediatricians (22.6 versus 5.1 per 1000 patient-days; P<0.0001), fewer significant clinical deterioration events (2.4 versus 0.43 per 1000 patient-days; P=0.013), reduced apprehension when calling the physician and no change in paediatrician workload. DISCUSSION: Implementation of the Bedside PEWS is feasible and safe, and may improve clinical outcomes.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".