Patient Characteristics and Disposition After Pediatric Medical Emergency Team (MET) Activation: Disposition Depends on Who Activates the Team
Bibliographic record
Abstract
OBJECTIVES: This study focused on health care staff (HCS) responsible for activating the medical emergency team (MET) at a pediatric tertiary hospital using a well-established rapid response system. Our goals were to report the patient characteristics, MET interventions, and disposition by activating HCS. METHODS: This is a retrospective cohort study of pediatric patients who received MET activation at the Children's Hospital of Eastern Ontario in Ottawa, Canada. Data were obtained from a prospectively maintained rapid response system database. The primary outcome was PICU admission, with the number and type of interventions performed as secondary outcomes. RESULTS: The most common MET activators were physicians (410, 53.3%) with nurses generating a comparable number (367, 47.7%). Significant differences in PICU admission rates were observed between activator groups, with physicians having statistically higher PICU admission rates when compared with nurses (25.2% vs 15.0%, P = .001). Compared with physicians, nursing-led activations on surgical patients had significantly lower odds of PICU admission relative to medical patients (odds ratio 0.19 vs 0.67; P = .03). No significant difference was observed in the type or number of interventions between any subgroup based on patient (surgery vs medical) or activator type. CONCLUSIONS: This study suggests that when nurses activate MET, patients are less likely to be transferred to the PICU despite receiving similar type and number of interventions. Our study results may help direct education initiatives aimed at enhancing the effectiveness of the afferent limb through informing specific HCS as to the importance of their role in using the MET.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".