Modified early obstetric warning system as a predictor of maternal morbidity in Papua New Guinea: a prospective study in Alotau Provincial Hospital
Bibliographic record
Abstract
Introduction Maternal mortality remains a significant global health issue, particularly in low- and middle-income countries (LMICs) like Papua New Guinea (PNG), where the maternal mortality ratio (MMR) remains high. The Modified Early Obstetric Warning System (MEOWS), a track-and-trigger tool, has been adopted internationally to facilitate early detection of obstetric complications. This study aims to validate MEOWS as a tool for predicting maternal morbidity, emergency interventions and mortality in a resource-limited setting at Alotau Provincial Hospital in PNG. Methodology This prospective observational study was conducted at Alotau Provincial Hospital over 17 months (July 2018 to November 2019). All pregnant women between 20 weeks of gestation and 6 weeks postpartum who required inpatient care were included. The MEOWS chart was used to monitor physiological parameters, triggering alerts for potential morbidity. The primary outcomes were maternal morbidity, emergency interventions, and mortality. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the tool’s effectiveness. Results A total of 676 MEOWS charts were analysed. Of these, 145 women (21%) triggered an alert. Women who triggered alerts had a significantly higher risk of developing obstetric morbidity (20% vs < 1%, P < 0.0000001), requiring high dependency unit (HDU) admission (19% vs < 1%, P < 0.0000001), and experiencing maternal death (8% vs < 1%, P < 0.0000001). The most common morbidities were hypertensive disorders (40%), haemorrhage (27%), and maternal sepsis (27%). The overall sensitivity and specificity of the MEOWS chart were 97%, with a PPV of 88% and NPV of 99%. Conclusion The MEOWS chart demonstrated high sensitivity and specificity in predicting maternal morbidity in a low-resource setting. The tool successfully identified women at risk of severe complications, including maternal death. These findings support the implementation of MEOWS in similar resource-limited settings, where timely interventions could significantly reduce maternal morbidity and mortality.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".