An audit of 70 maternal deaths.
Bibliographic record
Abstract
INTRODUCTION: Although women rarely die during pregnancy and childbirth in Denmark, keeping track of the maternal mortality rate and causes of death is vital in identifying learning points for future management of critical illness among obstetric patients and in pinpointing risk factors. METHODS: We identified maternal deaths between 2002 and 2017 by linking four Danish national health registers, using death certificates and reports from hospitals. An audit group then categorised each case by cause of death before identifying any suboptimal care and learning points, which may serve as a foundation for national guidelines and educational strategies. RESULTS: Seventy women died during pregnancy or within six weeks of a pregnancy in the study period. The most frequent causes of death were cardiovascular disease (n = 14), hypertensive disorder (n = 10), suicide (n = 10) and thromboembolism (n = 7). Suboptimal care was identified in 30 of the 70 cases. CONCLUSIONS: Mortality from some of the most important causes of death decreased during the study period. No deaths from preeclampsia or thrombosis, two of the leading causes of death, were identified after 2011. In 2015-2017, suicide was the main cause of maternal death, which indicates that a stronger focus on vulnerability in pregnancy and childbirth is essential. Among the 70 deaths, 34% were potentially avoidable, indicating that it is essential continuously to focus on how to reduce severe maternal morbidity and mortality. FUNDING: none TRIAL REGISTRATION. not relevant.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".