Maternal near miss and maternal death in the 2005 WHO global survey on maternal and perinatal health
Bibliographic record
Abstract
OBJECTIVE: To develop an indicator of maternal near miss as a proxy for maternal death and to study its association with maternal factors and perinatal outcomes. METHODS: In a multicenter cross-sectional study, we collected maternal and perinatal data from the hospital records of a sample of women admitted for delivery over a period of two to three months in 120 hospitals located in eight Latin American countries. We followed a stratified multistage cluster random design. We assessed the intra-hospital occurrence of severe maternal morbidity and the latter's association with maternal characteristics and perinatal outcomes. FINDINGS: Of the 97,095 women studied, 2964 (34 per 1000) were at higher risk of dying in association with one or more of the following: being admitted to the intensive care unit (ICU), undergoing a hysterectomy, receiving a blood transfusion, suffering a cardiac or renal complication, or having eclampsia. Being older than 35 years, not having a partner, being a primipara or para > 3, and having had a Caesarean section in the previous pregnancy were factors independently associated with the occurrence of severe maternal morbidity. They were also positively associated with an increased occurrence of low and very low birth weight, stillbirth, early neonatal death, admission to the neonatal ICU, a prolonged maternal postpartum hospital stay and Caesarean section. CONCLUSION: Women who survive the serious conditions described could be pragmatically considered cases of maternal near miss. Interventions to reduce maternal and perinatal mortality should target women in these high-risk categories.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".