Prevalence and Determinants of Maternal Mortality in Southeastern Iran (2013 - 2017): A Retrospective Cross-sectional Study
Bibliographic record
Abstract
Background: Recognizing the factors affecting maternal death can lead to the adoption of strategies to prevent similar deaths. Objectives: This study was performed to investigate the prevalence and causes of pregnant mothers' death in the population covered by Zahedan University of Medical Sciences. Methods: In this retrospective, descriptive, cross-sectional study, the files of 126 pregnant mothers who died during 2013 - 2017 were evaluated. Demographic and obstetrics information and variables related to maternal mortality, such as maternal mortality ratio (MMR), the cause of mother’s death, the time of mother’s death, and place of death, were evaluated in general and separately in each city (i.e., Zahedan, Khash, Saravan, and Chabahar) based on descriptive statistics and according to the nature of the variables. Results: Maternal mortality ratio in Zahedan was 174.96 per 100,000 case, in Khash 190.56 per 100,000 cases, in Saravan 371.87 per 100,000 cases, and in Chabahar 384.03 per 100,000 cases. Bleeding was the most common cause of death (42.53%), 61.9% of pregnant women were living in rural areas, 80.2% died in the third trimester of pregnancy, and 42.9% died in first 24 hours after delivery. The most common underlying disease was hypertension, 70.6% of mothers died in hospitals, and 47.6% were illiterate. The most common cause of maternal death in Zahedan was cardiac disease, in Khash it was hemolysis, elevated liver enzymes and low platelets (HELLP) syndrome, eclampsia, and preeclampsia, and in Saravan and Chabahar the leading cause was bleeding. Conclusions: Maternal mortality ratio was high in Sistan and Baluchestan. The investigation of the causes of maternal deaths showed that some of these deaths are avoidable. It is also necessary to improve midwifery emergencies management with intensive monthly courses to increase team capabilities for making the best use of golden time measures.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".