Prognostic accuracy of the Hamilton Early Warning Score (HEWS) and the National Early Warning Score 2 (NEWS2) among hospitalized patients assessed by a rapid response team
Bibliographic record
Abstract
BACKGROUND: Rapid response teams (RRTs) respond to hospitalized patients experiencing clinical deterioration and help determine subsequent management and disposition. We sought to evaluate and compare the prognostic accuracy of the Hamilton Early Warning Score (HEWS) and the National Early Warning Score 2 (NEWS2) for prediction of in-hospital mortality following RRT activation. We secondarily evaluated a subgroup of patients with suspected infection. METHODS: We retrospectively analyzed prospectively collected data (2012-2016) of consecutive RRT patients from two hospitals. The primary outcome was in-hospital mortality. We calculated the number needed to examine (NNE), which indicates the number of patients that need to be evaluated in order to detect one future death. RESULTS: Five thousand four hundred ninety-one patients were included, of whom 1837 (33.5%) died in-hospital. Mean age was 67.4 years, and 51.6% were male. A HEWS above the low-risk threshold (≥ 5) had a sensitivity of 75.9% (95% confidence interval (CI) 73.9-77.9) and specificity of 67.6% (95% CI 66.1-69.1) for mortality, with a NNE of 1.84. A NEWS2 above the low-risk threshold (≥ 5) had a sensitivity of 84.5% (95% CI 82.8-86.2), and specificity of 49.0% (95% CI: 47.4-50.7), with a NNE of 2.20. The area under the receiver operating characteristic curve (AUROC) was 0.76 (95% CI 0.75-0.77) for HEWS and 0.72 (95% CI: 0.71-0.74) for NEWS2. Among suspected infection patients (n = 1708), AUROC for HEWS was 0.79 (95% CI 0.76-0.81) and for NEWS2, 0.75 (95% CI 0.73-0.78). CONCLUSIONS: The HEWS has comparable clinical accuracy to NEWS2 for prediction of in-hospital mortality among RRT patients.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".