Monitoring maternal near miss/severe maternal morbidity: A systematic review of global practices
Bibliographic record
Abstract
There is international interest in monitoring severe events in the obstetrical population, commonly referred to as maternal near miss or severe maternal morbidity. These events can have significant consequences for individuals in this population and further study can inform practices to reduce both maternal morbidity and mortality. Numerous surveillance systems exist but we lack a standardized approach. Given the current inconsistencies and the importance in monitoring these events, this study aimed to identify and compare commonly used surveillance methods. In June 2018, we systematically searched MEDLINE, EMBASE, and CINAHL using terms related to monitoring/surveillance and maternal near miss/severe maternal morbidity. We included papers that used at least three indicators to monitor for these events and collected data on specific surveillance methods. We calculated the rate of maternal near miss/severe maternal morbidity in hospitalization data obtained from the 2016 US National Inpatient Sample using five common surveillance methods. Of 18,832 abstracts, 178 papers were included in our review. 198 indicators were used in studies included in our review; 71.2% (n = 141) of these were used in <10% of included studies and only 6.1% (n = 12) were used in >50% of studies included in our review. Eclampsia was the only indicator that was assessed in >80% of included studies. The rate of these events in American hospitalization data varied depending on the criteria used, ranging from 5.07% (95% CI = 5.02, 5.11) with the Centers for Disease Control criteria and 7.85% (95% CI = 7.79, 7.91) using the Canadian Perinatal Surveillance System. Our review highlights inconsistencies in monitoring practices within and between developed and developing countries. Given the wide variation in monitoring approaches observed and the likely contributing factors for these differences, it may be more feasible for clinical and academic efforts to focus on standardizing approaches in developed and developing countries independently at this time. PROSPERO Registration: CRD42018096858.
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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 | 0.018 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".