The Role of Laboratory Medicine in Improving Maternal Health Outcomes and Reducing Disparities
Bibliographic record
Abstract
Maternal mortality, or deaths due to complications from pregnancy or childbirth, is a global health issue. While maternal deaths worldwide decreased from 451 000 in 2000 to 287 000 in 2020, the global maternal mortality ratio (MMR) remains at an astoundingly high 223 deaths per 100 000 live births, or one maternal death every 2 minutes (1). Low-income countries bear the brunt of this affliction, with an MMR of 430 per 100 000 births compared to 13 per 100 000 in high-income countries (1). The most common causes of maternal death include postpartum hemorrhage, hypertensive disorders, sepsis, complications of unsafe abortion, and exacerbation of preexisting medical conditions due to pregnancy. Tragically, most of these deaths are preventable through access to skilled medical providers and interventions throughout pregnancy and after childbirth (2). The United States performs worse on measures of maternal mortality than any other high-income country. In 2020, the MMR in the United States was 21 per 100 000 births, 40% higher than the next-highest country, Chile (15 per 100 000 births), 91% higher than Canada (11 per 100 000), and 110% higher than the United Kingdom (10 per 100 000) (3). It is of concern that many studies have reported an increase in the US MMR over the past 2 decades. However, a recent study in the American Journal of Preventive Medicine (4) reported a particularly shocking finding: the MMR in the United States accelerated from 18.9 in 2019 to 31.8 per 100 000 births in 2021, representing a 68% increase in the span of 2 years and a 230% increase from the 9.7 deaths per 100 000 US births reported in 2002.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".