The Predictive Value of Vital Sign Patterns for Morbidity in Pregnancy: A Retrospective Cohort Study
Bibliographic record
Abstract
Objective. This study examined the predictive ability of established Maternal Early Warning systems (MEWS) for different types of maternal morbidity, in order to discern an optimal early warning system. Design. Retrospective cohort study. Setting. Four-hospital urban academic system. Population. All patients admitted to the obstetric services of this hospital system in 2018. Methods. All patient vital signs were collected and three sets of published MEWS criteria were evaluated in relation to maternal morbidity. The test characteristics of each MEWS, as well as for heart rate, blood pressure, and oxygen saturation individually and in different combinations were compared. Main Outcome Measures. Maternal morbidity, defined as a composite of hemorrhage, infection, acute cardiac disease, and acute respiratory disease, ascertained from informatics and administrative data. Results. Of 14,597 obstetric admissions, 2,451 patients experienced composite morbidity (16.8%). The sensitivities (15.3% - 64.8%), specificities (56.8% - 96.1%), and positive predictive values (22.3% - 44.5%) of the three MEWS criteria ranged. Of patients with any morbidity, 28% met criteria for the most liberal vital sign combination, while only 2% met criteria for the most restrictive parameters, compared to 14% and 1% of patients without morbidity, respectively. Sensitivity of all vital sign combinations was low (maximum 28.2%), while specificity ranged from 86.1 – 99.3%. Conclusions. Though all MEWS criteria demonstrated poor sensitivity for maternal morbidity, permutations of the most abnormal vital signs have high specificity, suggesting that MEWS may be better implemented as a trigger tool to target more sensitive screening techniques for maternal morbidity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".