Evaluation of Maternal Mortality Cases in the Province of Elazig, Turkey, 2007-2013: A Retrospective Study
Bibliographic record
Abstract
The aim of this study was to determine the causes and factors influencing maternal mortality. All maternal deaths occurring between January 2007 and November 2013 in the Elazig Province of Turkey were retrospectively investigated. The maternal age, obstetric history, cause of death, encountered delay model of each case, as well as the overall number of annual live births in the Province were determined. The information of cases was obtained from Directorate of Public Health and hospital records. Families or family doctors were also interviewed to obtain details about the circumstances surrounding each death. There were a total of 64,423 live births in the Province of Elazig between 2007- 2013. The number and ratio of maternal deaths due to direct and indirect causes were 12 and 18.6, respectively. The direct causes of maternal death were hypertensive diseases of pregnancy (n=5, 41.7%), obstetric hemorrhages (n=3, 25%) and pulmonary embolism (n=1, 8.3%). The indirect causes of death were cardiac diseases (n=2, 16.7%) and malignancy (n=1, 8.3%). When classified according to the "Three Delays Model", 2 cases were in the first delay model and 3 cases in the third delay model; the second delay model led to no maternal deaths. Hypertensive diseases of pregnancy are the leading cause of maternal mortality in our province. The preventable causes of maternal mortality and factors contributing to death must be identified to reduce the incidence.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".