Maternal near-miss and the risk of adverse perinatal outcomes: a prospective cohort study in selected public hospitals of Addis Ababa, Ethiopia
Bibliographic record
Abstract
BACKGROUND: Presence of maternal near-miss conditions in women is strongly associated with the occurrence of adverse perinatal outcomes, but not well-understood in low-income countries. The study aimed to ascertain the effect of maternal near-miss on the risk of adverse perinatal outcomes in Ethiopia. METHODS: A prospective cohort study was conducted in five public hospitals of Addis Ababa, Ethiopia. Women admitted from May 1, 2015 to April 30, 2016 were recruited for the study. We followed a total of 828 women admitted for delivery or treatment of pregnancy-related complications along with their singleton newborn babies. Maternal near-miss was the primary exposure and was ascertained using the World Health Organization criteria. Women who delivered without complications were taken as the non-exposed groups. The main outcome was adverse perinatal outcomes. Data on maternal near-miss and perinatal outcomes were abstracted from medical records of the participants. Exposed and non-exposed women were interviewed by well-trained data collectors to obtain information about potential confounding factors. Logistic regressions were performed using Stata version 13.0 to determine the adjusted odds of adverse perinatal outcomes. RESULTS: A total of 207 women with maternal near-miss and 621 women with uncomplicated delivery were included in the study. After adjusting for potential confounders, women with maternal near-miss condition had more than five-fold increased odds of adverse perinatal outcomes compared to women who delivered without any complications (AOR = 5.69: 95% CI; 3.69-8.76). Other risk factors that were independently associated with adverse perinatal outcomes include: rural residence, history of prior stillbirth and primary educational level. CONCLUSIONS: Presence of maternal near-miss in women is an independent risk factor for adverse perinatal outcomes. Hence, interventions rendered at improvement in maternal health of Ethiopia can lead to an improvement in perinatal outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".