Introduction of a standardised maternity early warning system: indicative data from a before‐and‐after study at a large pilot site before national rollout in Aotearoa New Zealand
Bibliographic record
Abstract
Strong evidence now demonstrates that recognition and response systems using standardised early warning scores can help prevent harm associated with in-hospital clinical deterioration in non-pregnant adult patients. However, a standardised maternity-specific early warning system has not yet been agreed in the UK. In Aotearoa New Zealand, following the nationwide implementation of the standardised New Zealand Early Warning Score (NZEWS) for adult inpatients, a modified maternity-specific variation (NZMEWS) was piloted in a major tertiary hospital in Auckland, before national rollout. Following implementation in July 2018, we observed a significant and sustained reduction in severe maternal morbidity as measured by emergency response calls to women who were very unwell (emergency response team call), and a non-significant reduction in cardiorespiratory arrest team calls. Emergency response team calls to maternity wards fell from a median of 0.8 per 100 births at baseline (January 2017-May 2018) to 0.6 per 100 births monthly (from March 2019 to December 2020) (p < 0.0001). Cardiorespiratory arrest team calls to maternity wards fell from 0.14 per 100 births per quarter (quarter 1 2017-quarter 2 2018) to 0.09 calls per 100 births per quarter after NZMEWS was introduced (quarter 3 2018-quarter 4 2020) (p = 0.2593). These early results provide evidence that NZMEWS can detect and prevent deterioration of pregnant women, although there are multiple factors that may have contributed to the reduction in emergency response calls noted.
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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".