Incidence and Causes of Maternal Mortality in the Unites States [294]
Bibliographic record
Abstract
INTRODUCTION: Maternal mortality rates have been increasing in recent years in the United States. We sought to identify the most prevalent causes of maternal deaths and examine trends from 2003 to 2011 using a population-based cohort. METHODS: We carried out a population-based retrospective cohort study using data obtained from the Health Care Cost and Utilization Project, Nationwide Inpatient Sample on all women who delivered a neonate, died in pregnancy, or both. Causes of death were determined through case-by-case review of individual patient records. Three-year intervals were used to identify evolving trends for causes of death. RESULTS: There were 1,102 maternal deaths among 7,785,583 births between 2003 and 2011 for an overall maternal mortality of 1.42 (1.33–1.50) per 10,000 pregnancies. The most common overall attributable causes of maternal death included sepsis (21.1%), cardiac disease (18.4%), hemorrhage (15.9%), venous thromboembolism (15.5%), and hypertensive disorders (10%). Over the course of the study period, overall maternal death rates remained stable; however, deaths resulting from hemorrhage increased from 8% to 21.5%, for hypertensive disorders from 3.7% to 15.1%, and decreased for sepsis-related deaths from 33.7% to 10.2% (P<.05). CONCLUSION: Maternal mortality remains a rare event. Sepsis rates are increasing and are becoming a predominant cause of maternal mortality. Further research on prevention of septic related morbidity needs to be conducted.
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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 | 0.002 |
| 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) | 0.002 | 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".