Triggers and Interventions of Patients Who Require Medical Emergency Team Reviews: A Cross-Sectional Analysis of Single Versus Multiple Reviews
Bibliographic record
Abstract
BACKGROUND: Medical emergency teams constitute part of the escalation protocol of early warning systems in many hospitals. The literature indicates that medical emergency teams may reduce hospital mortality and cardiac arrest. A greater understanding of pathways of patients who experience multiple medical emergency team reviews will inform clinical decision-making. OBJECTIVES: To explore differences between patients who require a single medical emergency team review and those who require multiple reviews, and to identify any differences between patients who were reviewed only once during admission and patients who required multiple reviews. METHODS: Data for this retrospective cross-sectional review, including demographic data, call triggers, outcomes, and interventions, were routinely collected from January 2013 through December 2015. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) collaborative's cross-sectional studies checklist (version 4). RESULTS: Of 54 787 admitted patients, 1274 (2%) required a call to a medical emergency team; of those, 260 patients (20%) needed multiple calls. Patients requiring multiple calls demonstrated higher mortality (odds ratio, 1.49 [95% CI, 1.12-1.98]). A logistic regression model identified surgical patients and those receiving antibiotics and respiratory interventions at the first medical emergency team review as being more likely to require multiple reviews. Patients transferred to a higher level of care after the first review were less likely to require another review. CONCLUSIONS: Patients requiring multiple medical emergency team reviews have higher mortality. Surgical patients have a higher risk of requiring multiple reviews. Hospitals need to include more details on surgical patients when auditing medical emergency team activation.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".