Reduction in maternal mortality in Denmark over three decades.
Bibliographic record
Abstract
INTRODUCTION: Women very rarely die during pregnancy and childbirth in Denmark. Although maternal deaths are registered worldwide, various studies indicate that underreporting does occur. This paper presents validated Danish register data for two periods between 1985 and 2017. METHODS: Maternal deaths were identified from 1985 to 1994 and from 2002 to 2017 by linking four national health registers, death certificates and notifications from maternity wards. A group of obstetricians categorised and assessed all medical records, classifying each case by cause of death. RESULTS: Linkage of four registers yielded valid data, leading to the identification of 143 maternal deaths in the abovementioned periods. From 1985-1994 there were 73 deaths and 618,021 live births, resulting in a maternal mortality rate of 11.8 per 100,000 live births with a non-significant 2% annual increase (95% confidence interval (CI): -6.0-11.0%). From 2002 to 2017 there were 70 maternal deaths and 999,206 live births, resulting in a maternal mortality rate of 7.0 per 100,000 live births (95% CI: 5.5-8.9) with a significant 9% annual decrease (95% CI: 4.0-14.0%). CONCLUSIONS: Overall maternal mortality decreased in the course of the two periods (n = 33 years), with a significant decrease during the last period. This is suggested to be a result of multiple clinical and organisational improvements as discussed in the paper. 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".