Can Early Warning Systems Enhance Detection of High Risk Patients by Rapid Response Teams?
Bibliographic record
Abstract
PURPOSE: We sought to evaluate if incorporating an early warning system (EWS), the Visensia Safety Index (VSI) and the National Early Warning Systems 2 (NEWS2), may lead to earlier identification of rapid response team (RRT) patients. METHODS: This was a retrospective study (2015-2018) of patients experiencing RRT activation within a tertiary care network. We evaluated the proportion of patients with an EWS alert prior to RRT activation and their associated outcomes (primary: hospital mortality). RESULTS: There were 6,346 RRT activations over the study period. Of these, 2042 (50.8%) patients would have had a VSI alert prior to RRT activation, with a median advanced time of 3.6 (IQR 0.5-12.8) hours, compared to 2351 (58.4%) patients and 9.8 (IQR 2.0-18.7) hours for NEWS2. Patients with a potential alert prior to RRT activation had an increased odds of mortality for both VSI (OR 1.2, 95%CI 1.1-1.3) and NEWS2 (OR 2.7, 95% CI 2.4-3.1). Prognostic accuracy for hospital mortality was similar between groups. CONCLUSION: Utilization of an EWS by an RRT has potential to provide earlier recognition of deterioration and mortality risk among hospitalized inpatients.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".