Validating the Performance of the Modified Early Obstetric Warning System Multivariable Model to Predict Maternal Intensive Care Unit Admission
Bibliographic record
Abstract
OBJECTIVES: To evaluate the performance of the Modified Early Obstetric Warning System (MEOWS) to predict maternal ICU admission in an obstetric population. DESIGN: Case-control study. SETTING: Two maternity units in Vancouver, Canada, one with ICU facilities, between January 1, 2000, and December 31, 2011. PATIENTS: Pregnant or recently delivered (≤6 weeks) women admitted to the hospital for >24 hours. Three control patients were randomly selected per case and matched for year of admission. MEASUREMENTS AND MAIN RESULTS: Retrospective, observational, case-control validation study investigating the physiologic predictors of admission in the 24-hour period preceding either ICU admission >24 hours (cases) or following admission (control patients). Model performance was assessed based on sensitivity, specificity, and predictive values. Forty-six women were admitted to the ICU for >24 hours (0.51/1000 deliveries); the study included 138 randomly selected control patients. There were no maternal deaths in the cohort. MEOWS had high sensitivity (0.96) but low specificity (0.54) for ICU admission >24 hours, whereas ≥1 one red trigger maintained sensitivity (0.96) and improved specificity (0.73). CONCLUSION: Altering MEOWS trigger parameters may improve the accuracy of MEOWS in predicting ICU admission. Formal modelling of a MEOWS scoring system is required to support evidence-based care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".