Global and regional estimates of maternal near miss: a systematic review, meta-analysis and experiences with application
Bibliographic record
Abstract
INTRODUCTION: Maternal near miss (MNM) is a useful means to examine quality of obstetric care. Since the introduction of the WHO MNM criteria in 2011, it has been tested and validated, and is being used globally. We sought to systematically review all available studies using the WHO MNM criteria to develop global and regional estimates of MNM frequency and examine its application across settings. METHODS: We conducted a systematic review by implementing a comprehensive literature search from 2011 to 2018 in six databases with no language restrictions. The predefined data collection tool included sections on study characteristics, frequency of near-miss cases and study quality. Meta-analysis was performed by regional groupings. Reported adaptations, modifications and remarks about application were extracted. RESULTS: 7292 articles were screened by title and abstract, and 264 articles were retrieved for full text review for the meta-analysis. An additional 230 articles were screened for experiences with application of the WHO MNM criteria. Sixty studies with near-miss data from 56 countries were included in the meta-analysis. The pooled global near-miss estimate was 1.4% (95% CI 0.4% to 2.5%) with regional variation in MNM frequency. Of the 20 studies that made adaptations to the criteria, 19 were from low-resource settings where lab-based criteria were adapted due to resource limitations. CONCLUSIONS: The WHO MNM criteria have enabled the comparison of global and sub-national estimates of MNM frequency. There has been good uptake in low-resource countries but contextual adaptations are necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.139 | 0.282 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".